Cleavage and polyadenylation of a precursor mRNA is important for transcription termination, mRNA stability, and regulation of gene expression. This process is directed by a multitude of protein factors and cis elements in the pre-mRNA sequence surrounding the cleavage and polyadenylation site. Importantly, the location of the cleavage and polyadenylation site helps define the 3’ untranslated region of a transcript, which is important for regulation by microRNAs and RNA binding proteins. Additionally, these sites have generally been poorly annotated. To identify 3’ ends, many techniques utilize an oligo-dT primer to construct deep sequencing libraries. However, this approach can lead to identification of artifactual polyadenylation sites due to internal priming in homopolymeric stretches of adenines. Previously, simple heuristic filters relying on the number of adenines in the genomic sequence downstream of a putative polyadenylation site have been used to remove these sites of internal priming. However, these simple filters may not remove all sites of internal priming and may also exclude true polyadenylation sites. Therefore, I developed a naïve Bayes classifier to identify putative sites from oligo-dT primed 3’ end deep sequencing as true or false/internally primed. Notably, this algorithm uses a combination of sequence elements to distinguish between true and false sites. Finally, the resulting algorithm is highly accurate in multiple model systems and facilitates identification of novel polyadenylation sites.

No description available.

空載雷射掃描為一可快速獲取地面物體三維空間資訊之技術，而新型發展之全波形（Full-Waveform）系統可完整記錄雷射回波訊號之波形，透過波形偵測與波形擬合等資料前處理，可得到代表地物獨特反射特性的波形參數資料，包括振幅值（Amplitude）、波形寬（Pulse-width）與後續計算之散射截面積係數（Backscatter cross-section coefficient）。 得到各點位之波形資料後，將以波形資料為主進行位於山區之實驗區地物分類，並將使用由實驗區航照影像提供之RGB波段光譜資料計算之綠度指數（Greenness）與計算影像灰階統計值之紋理參數如均質度（Homogeneity）、熵值（Entropy）與R波段平均值（Mean）等參數輔助分類。分類進行之前，透過抽樣實驗區候選地類包括樹林、草地、道路與樹種建物，並以貝氏定理（Bayes Theorem）分析計算不同地物類別在各分類參數區間內的貝氏機率，接著以多項式函數擬合各地類在不同參數之貝氏機率曲線，並以計算反曲點之方式自動化決定該分類參數之門檻值區間。 分類成果顯示，全波形系統提供之波形資料對於受上層植物遮蔽與陰影區之植物點與道路點之分類有顯著之成果，且透過物體對於波形資料之反射特性不同，具備應用於區別不同建築材質類別之潛力。 / Airborne Laser Scanning is a technique capable of acquiring 3D information of land objects. The latest full-waveform system is further improved with the ability of recording complete waveform of reflected laser signal. After the preprocessing procedures such as pulse detection and pulse fitting, the waveform information including amplitude, pulse width and backscatter cross-section were derived. Such information was valuable as they represented unique properties of land objects. In this study, waveform information of all scanned points were utilized to classify land cover in the test area located in mountain area. Additionally, the Greenness value as well as the texture parameters such as Homogeneity, Entropy and Mean of R band calculated from the ortho-image were used for classification. We aimed to classify the point cloud into vegetation, road and building categories. The Bayes Theorem was used to determine the threshold range of each parameters for classification. As a result, the waveform information were useful for classifying road points covered by upper vegetation points and also vegetation and road points located in shadow area. Moreover, through the analysis of reflective properties of different object using waveform parameters, it was of potential to be applied to distinguish material of buildings.

In the paper at hand we apply it to Bayesian statistics to obtain "Fuzzy Bayesian Inference". In the subsequent sections we will discuss a fuzzy valued likelihood function, Bayes' theorem for both fuzzy data and fuzzy priors, a fuzzy Bayes' estimator, fuzzy predictive densities and distributions, and fuzzy H.P.D .-Regions. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

No description available.

在植被覆蓋的山區中，由於空載雷射掃描可穿透植被間縫隙的特性，有較高機會收集到植被下的地面資訊，因此適合作為製作植被覆蓋地區數值高程模型的資料來源，而在過濾過程中，一般僅利用點雲間的三維位置關係進行幾何過濾，而全波形空載雷射掃描可另外提供點位的波形寬、振幅值、散射截面積以及散射截面積數等波形資料，本研究將透過波形資料分析進行點雲過濾。 首先經最低點採樣後，本研究利用貝氏定理自動分析並計算得到地面點的波形資料的特徵區間範圍，採用振幅值、散射截面積以及散射截面積係數得到的特徵區間範圍開始第一階段的波形資料過濾，完成後再以第二階段的一般幾何過濾濾除剩餘之非地面點，最後的成果將與航測以及只採用幾何過濾時的成果比較。 由研究成果中顯示，不同的植被覆蓋間的單一回波波形資料的差異較明顯，最後回波類似。同一植被覆蓋下的單一回波及最後回波反應不同。而在成果的比較中，本實驗的成果與不採用波形資料輔助的成果大致相同本研究的成果在部分植被覆蓋的區域成果稍差，但透過波形過濾，可將幾何過濾所需計算的點雲數減少許多，可以增進整理過濾的效率。本研究的成果與航測相比時，在植被覆蓋區域較航測成果貼近實際的地面起伏，數值高程模型成果較為正確。 / In mountain areas covered with vegetation, discrete airborne laser scanning is an appropriate technique to produce DEMs for its laser signal is able to reach the ground beneath the vegetation. Once the scanned data was derived, point cloud filtering was performed based on the geometry relationship between the points at the processing stage. With the development of the advanced full-waveform laser scanning system, the additional waveform data has been proved useful for improving the performance of point cloud filtering. This research therefore focused on using the waveform data to extract DEM over vegetation covered area. The amplitude, backscatter cross-section and backscatter cross-section coefficient were the waveform parameters used to do the filtering. After initial waveform analysis was accomplished, an automated method to determine threshold range of each parameter representing ground points was proposed. By applying the thresholds, the original point cloud was filtered. Geometric filtering method was then used to eliminate the remained non-ground points. As a result, the DEM over the target vegetated area was derived. With the comparison against photogrammetric DEM and DEM derived from traditional filtering method, it was demonstrated that the quality of the resultant DEM was improved.

Esta tesis tiene dos objetivos principales. El primero es proponer métodos multivariantes para el estudio de las desigualdades socioeconómicas en la mortalidad en áreas pequeñas. El segundo es estudiar estas desigualdades en la práctica en varias ciudades españolas. En consecuencia, se han realizado cuatro estudios diferentes: dos de ellos más metodológicos y los otros dos más aplicados al estudio de las desigualdades. El primer estudio metodológico propone usar Análisis Factorial Bayesiano para el cálculo de índices de privación. Además, en este estudio se concluye que ignorar la variabilidad en la estimación del índice puede conducir a un sesgo cuando las áreas se agrupan según cuantiles del índice. En el segundo estudio se ha reformulado el modelo SANOVA de modo que es posible introducir una covariable dentro de este modelo. Asimismo, dicha reformulación permite la descomposición de la varianza de los patrones estudiados como suma de varianzas de todas las componentes del modelo. Finalmente, los estudios restantes evidencian la existencia de desigualdades socioeconómicas en la mortalidad total y en la mortalidad por las principales causas específicas en once ciudades españolas. Además, para las enfermedades isquémicas del corazón estas desigualdades parecen aumentar ligeramente en el tiempo. / This thesis has two main objectives. The first is to propose multivariate methods for the study of socioeconomic inequalities in mortality in small areas. The second is to study socioeconomic inequalities in mortality in small areas of several Spanish cities. Four different studies were conducted to attain these objectives: two of them focussed on the methodological aspects and the other two being empirical studies focussed on the study of inequalities. The first methodological study proposes the Bayesian factor analysis to calculate a deprivation index. Additionally, this study concludes that ignoring the uncertainty obtained in the estimation of the index may result in a misclassification bias when the areas are grouped according to quantiles of the index. In the second methodological study the SANOVA model has been reformulated enabling the introduction of a covariate in the model. Also, this reformulation permits the decomposition of the variance of the studied patterns into the sum of variances of all the model components. Finally, the other studies show the existence of socioeconomic inequalities in total mortality and mortality by specific causes in eleven major Spanish cities. In addition, for ischemic heart disease these inequalities appear to increase slightly over time.

With the prevalence of internet, it is gradually becoming a trend to use the network as a tool of computer-added education. However, it is used to present the computer-added education with static state of the word, but it is just convenient to read for the user and there are no difference with traditional textbook. As the growing up of WWW and the development of Java, the interactive computer-added education is becoming a trend in the future and it can promote the effect of teaching basic statistics with the application of this new media. The instructor can take advantage of HTML by combining with Java Applets to achieve the display of interactive education through WWW. In this paper, we will use six examples of Java Applets about statistical computer-added education to help student easily to learn and to understand some abstract statistical concepts. The key methods to reach the goal are visualization and simulation with the display of graphics or games. Finally, we will discuss how to use the Applets and how to add the Java Applets into your homepage easily.

Le véhicule électrique rassemble beaucoup d'équipements électrotechniques. Tous sont potentiellement source de champ magnétique dans l'habitacle : zone confinée où se trouvent les passagers. Il est illusoire de réaliser un modèle numérique complet tant le nombre de paramètres est important. Il est également impossible de placer des capteurs de champ partout à l'intérieur de l'habitacle. Après une étude approfondie des caractéristiques du champ magnétique mesuré dans l'habitacle, nous proposons d'allier modèle a priori et mesure de champ dans une approche Bayésienne du problème inverse. Basée sur le développement en harmonique sphérique du champ, l'apport d'information a priori oriente la solution et permet l'identification de nombreux paramètres avec très peu de mesure. / Electric vehicles have a lot of electrical devices onboard. All of them may generate electromagnetic field inside the car: a quite small space containing the passengers. A complete modeling of the vehicle can hardly be done because of the parameters number. The magnetic field measurement everywhere inside the car is also impossible. We first measure the magnetic field inside the car to study its characteristics. Then we propose to merge together a priori modeling with measurements into a Bayesian approach of the inverse problem. Based on spherical harmonic decomposition of the magnetic field, a priori information helps the resolution and gives the identified parameters with a very few measurements.

Smokeless powders are a set of energetic materials, known as low explosives, which are typically utilized for reloading ammunition. There are three types which differ in their primary energetic materials; where single base powders contain nitrocellulose as their primary energetic material, double and triple base powders contain nitroglycerin in addition to nitrocellulose, and triple base powders also contain nitroguanidine. Additional organic compounds, while not proprietary to specific manufacturers, are added to the powders in varied ratios during the manufacturing process to optimize the ballistic performance of the powders. The additional compounds function as stabilizers, plasticizers, flash suppressants, deterrents, and opacifiers. Of the three smokeless powder types, single and double base powders are commercially available, and have been heavily utilized in the manufacture of improvised explosive devices. Forensic smokeless powder samples are currently analyzed using multiple analytical techniques. Combined microscopic, macroscopic, and instrumental techniques are used to evaluate the sample, and the information obtained is used to generate a list of potential distributors. Gas chromatography – mass spectrometry (GC-MS) is arguably the most useful of the instrumental techniques since it distinguishes single and double base powders, and provides additional information about the relative ratios of all the analytes present in the sample. However, forensic smokeless powder samples are still limited to being classified as either single or double base powders, based on the absence or presence of nitroglycerin, respectively. In this work, the goal was to develop statistically valid classes, beyond the single and double base designations, based on multiple organic compounds which are commonly encountered in commercial smokeless powders. Several chemometric techniques were applied to smokeless powder GC-MS data for determination of the classes, and for assignment of test samples to these novel classes. The total ion spectrum (TIS), which is calculated from the GC-MS data for each sample, is obtained by summing the intensities for each mass-to-charge (m/z) ratio across the entire chromatographic profile. A TIS matrix comprising data for 726 smokeless powder samples was subject to agglomerative hierarchical cluster (AHC) analysis, and six distinct classes were identified. Within each class, a single m/z ratio had the highest intensity for the majority of samples, though the m/z ratio was not always unique to the specific class. Based on these observations, a new classification method known as the Intense Ion Rule (IIR) was developed and used for the assignment of test samples to the AHC designated classes. Discriminant models were developed for assignment of test samples to the AHC designated classes using k-Nearest Neighbors (kNN) and linear and quadratic discriminant analyses (LDA and QDA, respectively). Each of the models were optimized using leave-one-out (LOO) and leave-group-out (LGO) cross-validation, and the performance of the models was evaluated by calculating correct classification rates for assignment of the cross-validation (CV) samples to the AHC designated classes. The optimized models were utilized to assign test samples to the AHC designated classes. Overall, the QDA LGO model achieved the highest correct classification rates for assignment of both the CV samples and the test samples to the AHC designated classes. In forensic application, the goal of an explosives analyst is to ascertain the manufacturer of a smokeless powder sample. In addition, knowledge about the probability of a forensic sample being produced by a specific manufacturer could potentially decrease the time invested by an analyst during investigation by providing a shorter list of potential manufacturers. In this work, Bayes* Theorem and Bayesian Networks were investigated as an additional tool to be utilized in forensic casework. Bayesian Networks were generated and used to calculate posterior probabilities of a test sample belonging to specific manufacturers. The networks were designed to include manufacturer controlled powder characteristics such as shape, color, and dimension; as well as, the relative intensities of the class associated ions determined from cluster analysis. Samples were predicted to belong to a manufacturer based on the highest posterior probability. Overall percent correct rates were determined by calculating the percentage of correct predictions; that is, where the known and predicted manufacturer were the same. The initial overall percent correct rate was 66%. The dimensions of the smokeless powders were added to the network as average diameter and average length nodes. Addition of average diameter and length resulted in an overall prediction rate of 70%.