Population size estimation from mark-resighting surveys

We consider the problem of estimating the size of a closed population based on the results of a certain type of marking-resighting sampling design. The design is similar to the commonly used multiple capture-recapture design, yet in some cases economically more feasible and easy to use. Sampling is done by first tagging a number of randomly selected animals with visible markers and later randomly sighting them (for instance, for large animals by visually sampling from a helicopter) and counting the number of tagged animals; In this paper, by using the above mentioned sampling scheme we present the maximum likelihood estimation (MLE) and Bayesian estimation of N by using different methods and construct their confidence intervals. We also use real data sets and simulated data sets to test these statistical indexes, and we find that it is reasonable for us to construct the confidence intervals of N by using these methods and that the approximate confidence interval of N is reliable

Semantic Orientation, Syntactic Position and Pragmatic Function of Modifier in Chinese-English Translation

In Chinese-English translation the equivalents for noun or verb modifiers are more than often subject to redeployment in the target language. According to the principle that a modifier is supposed to be syntactically located in the immediacy of the modified toward which it is semantically oriented, the displacement of a modifier from where it should be syntactically located is incurred because of pragmatic motivations. However, in the context of Chinese-English translation, the modified can exert more drawing force on the modifier. As a result, the originally displaced modifier, now in a position identical to that of its English equivalent, returns to the modified, the noun or the verb, toward which it is semantically oriented. Or the modifier will resume the syntactic position as close to its modified noun or verb as possible. A conclusive analysis claims that the drawing gravity from the modified in C-E translation results from the translator’s heightened semantic concerns, although some pragmatic effects can be produced as expected or unexpected.Dans la traduction du chinois vers l’anglais, les équivalents des modificateurs du nom ou du verbe sont souvent sujets à un redéploiement dans la langue cible. En vertu du principe selon lequel un modificateur doit être situé syntaxiquement dans l’immédiateté du modifié vers lequel il est sémantiquement orienté, le déplacement d’un modificateur depuis sa position syntaxique de base est provoqué par des facteurs pragmatiques. Toutefois, dans le contexte de la traduction chinois-anglais, le modifié peut exercer une force d’attraction accrue sur le modificateur. En conséquence, le modificateur initialement déplacé, et donc en position identique à celle de son équivalent anglais, retourne dans une position proche du modifié, le nom ou le verbe, vers lequel il est sémantiquement orienté. Ou encore, le modificateur réintègre la position syntaxique le plus près du nom ou du verbe qu’il modifie. L’analyse conclut de manière convaincante que la force de gravité du modifié dans la traduction chinois-anglais est le fruit d’un accroissement des préoccupations sémantiques du traducteur, bien que certains effets pragmatiques, attendus ou inattendus, puissent aussi être produits

On lossless image compression

Accompanying the growth in the size of database and the use of images has been a large increase in the number of users and duration of usage by personnel at remote locations. These factors result in tremendous amounts of data being stored and transferred between computers and remote terminals. Data compression is therefore very important to reduce the size of data files and the time needed to transfer between the remote terminals. A wide variety of compression techniques have been developed over past 20 years to reduce size of image files. Some techniques represent the application of modified lossless compression algorithms while other techniques represent the application of the lossy algorithms; In this thesis, we discuss the lossless compression of the image with the algorithm of the mean square error analysis and other algorithm. We also discuss the statistical properties of the residual image. We use grayscale image files to test our results

Quantitative Analysis of Bleomycin in Rat Plasma by LC-MS/MS

Bleomycin is the most commonly used compound in its group of antineoplastic drugs. It works on tumor cells by single and double stranded DNA cleavage after its activation, in which it blocks tumor cells’ DNA replication or transcription activities to inhibit tumor cells’ growth. Bleomycin sulfate (Blenoxane) is the most popular preparation used in clinical research, and contains Bleomycin fractions of A2 and B2, which causes difficulties in quantitative analysis. This work uses the metal chelating property of Bleomycin as an advantage to simplify and improve sensitivity of existing quantitative methods. Copper was spiked in excess to plasma samples, followed by liquid-liquid extraction. Samples were then subjected to analysis by high-performance liquid chromatography electrospray ionization tandem mass spectrometry using a quadrupole trap mass analyzer. Samples spiked with copper showed improved selectivity over samples without excess copper, thereby making use of standard mass spectrometers a possibility in the clinic. In comparison with current methods of quantification of Bleomycin in plasma, this method achieved higher percent recoveries of the chemotherapy drug, higher sensitivity of quantification, with lower matrix effects, as well as a more simple preparation method. Linear range in the lower nanogram per milliliter range with a correlation coefficient over 0.99 makes this method promising for improved quantification and monitoring of Bleomycin in plasma

Identifying RNA splicing factors using IFT genes in Chlamydomonas reinhardtii

Intraflagellar transport moves proteins in and out of flagella/cilia and it is essential for the assembly of these organelles. Using whole-genome sequencing, we identified splice site mutations in two IFT genes, IFT81 ( fla9 ) and IFT121 ( ift121-2 ), which lead to flagellar assembly defects in the unicellular green alga Chlamydomonas reinhardtii . The splicing defects in these ift mutants are partially corrected by mutations in two conserved spliceosome proteins, DGR14 and FRA10. We identified a dgr14 deletion mutant, which suppresses the 3′ splice site mutation in IFT81 , and a frameshift mutant of FRA10 , which suppresses the 5′ splice site mutation in IFT121 . Surprisingly, we found dgr14-1 and fra10 mutations suppress both splice site mutations. We suggest these two proteins are involved in facilitating splice site recognition/interaction; in their absence some splice site mutations are tolerated. Nonsense mutations in SMG1 , which is involved in nonsense-mediated decay, lead to accumulation of aberrant transcripts and partial restoration of flagellar assembly in the ift mutants. The high density of introns and the conservation of noncore splicing factors, together with the ease of scoring the ift mutant phenotype, make Chlamydomonas an attractive organism to identify new proteins involved in splicing through suppressor screening. </jats:p

In-Vivo Lipidomics using Single-Cell Raman Spectroscopy

We describe a method for direct, quantitative, in vivo lipid profiling of oil producing microalgae using single-cell laser-trapping Raman spectroscopy (LTRS). This approach is demonstrated in the quantitative determination of the degree of unsaturation and transition temperatures of constituent lipids within microalgae. These properties are important markers for determining engine compatibility and performance metrics of algal biodiesel. We show that these factors can be directly measured from a single living microalgal cell held in place with an optical trap while simultaneously collecting Raman data. Cellular response to different growth conditions is monitored in real time. Our approach circumvents the need for lipid extraction and analysis that is both slow and invasive. Furthermore, this technique yields real-time chemical information in a label-free manner, thus eliminating the limitations of impermeability, toxicity and specificity of the fluorescent probes used in other common protocols. Although the single-cell Raman spectroscopy demonstrated here is focused on the study of the microalgal lipids with biofuel applications, the analytical capability and quantitation algorithms demonstrated are applicable to many different organisms, and should prove useful for a diverse range of applications in lipidomics

Perturbation-based Self-supervised Attention for Attention Bias in Text Classification

In text classification, the traditional attention mechanisms usually focus too much on frequent words, and need extensive labeled data in order to learn. This paper proposes a perturbation-based self-supervised attention approach to guide attention learning without any annotation overhead. Specifically, we add as much noise as possible to all the words in the sentence without changing their semantics and predictions. We hypothesize that words that tolerate more noise are less significant, and we can use this information to refine the attention distribution. Experimental results on three text classification tasks show that our approach can significantly improve the performance of current attention-based models, and is more effective than existing self-supervised methods. We also provide a visualization analysis to verify the effectiveness of our approach

Conditional Dynamic Mutual Information-Based Feature Selection

With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in the post-analysis process. Thus, it is necessary to eliminate irrelevant features from data before being fed into learning algorithms. Currently, many endeavors have been attempted in this field and many outstanding feature selection methods have been developed. Among different evaluation criteria, mutual information has also been widely used in feature selection because of its good capability of quantifying uncertainty of features in classification tasks. However, the mutual information estimated on the whole dataset cannot exactly represent the correlation between features. To cope with this issue, in this paper we firstly re-estimate mutual information on identified instances dynamically, and then introduce a new feature selection method based on conditional mutual information. Performance evaluations on sixteen UCI datasets show that our proposed method achieves comparable performance to other well-established feature selection algorithms in most cases