2,404 research outputs found
The Development Impact of Information Technology in Trade Facilitation
No full textThe main purpose of this chapter is to provide an overview and context of the country studies on Information Technology (IT) for Trade Facilitation (TF) in Small and Medium Enterprises (SMEs).Impact of Information Techonology, Trade Facilitation, SMEs
Evaluation of ITER TF Coil Joint Performance
No full textTo evaluate the ITER TF joint performance, the joint test sample, which consists of two short TF conductors and has full size joint, shall be tested using NIFS test facility under the condition of current of 68 kA and external field of 2 T. For high accuracy, the issue of voltage difference between cable and jacket had been anticipated in the evaluation of joint resistance. If a voltage difference exist between them, it is difficult to measure real joint resistance using voltage taps on the jacket. Therefore, the author first calculated the position where voltage of cable and jacket become equipotential and then decided the voltage tap position where the influence of voltage drop could be avoided. Thus, a high accuracy measurement of joint resistance could be achieved and the joint resistance was accurately evaluated as around 1 n Ω , which is well below the ITER requirement of 3 n Ω .journal articl
Inferring condition-specific targets of human TF-TF complexes using ChIP-seq data
No full textAbstract
Background
Transcription factors (TFs) often interact with one another to form TF complexes that bind DNA and regulate gene expression. Many databases are created to describe known TF complexes identified by either mammalian two-hybrid experiments or data mining. Lately, a wealth of ChIP-seq data on human TFs under different experiment conditions are available, making it possible to investigate condition-specific (cell type and/or physiologic state) TF complexes and their target genes.
Results
Here, we developed a systematic pipeline to infer Condition-Specific Targets of human TF-TF complexes (called the CST pipeline) by integrating ChIP-seq data and TF motifs. In total, we predicted 2,392 TF complexes and 13,504 high-confidence or 127,994 low-confidence regulatory interactions amongst TF complexes and their target genes. We validated our predictions by (i) comparing predicted TF complexes to external TF complex databases, (ii) validating selected target genes of TF complexes using ChIP-qPCR and RT-PCR experiments, and (iii) analysing target genes of select TF complexes using gene ontology enrichment to demonstrate the accuracy of our work. Finally, the predicted results above were integrated and employed to construct a CST database.
Conclusions
We built up a methodology to construct the CST database, which contributes to the analysis of transcriptional regulation and the identification of novel TF-TF complex formation in a certain condition. This database also allows users to visualize condition-specific TF regulatory networks through a user-friendly web interface
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks
No full textTerm frequency-inverse document frequency, or TF-IDF for short, and its many variants form a class of term weighting functions the members of which are widely used in text analysis applications. While TF-IDF was originally proposed as a heuristic, theoretical justifications grounded in information theory, probability, and the divergence from randomness paradigm have been advanced. In this work, we present an empirical study showing that TF-IDF corresponds very nearly with the hypergeometric test of statistical significance on selected real-data document retrieval, summarization, and classification tasks. These findings suggest that a fundamental mathematical connection between TF-IDF and the negative logarithm of the hypergeometric test P-value (i.e., a hypergeometric distribution tail probability) remains to be elucidated. We advance the empirical analyses herein as a first step toward explaining the long-standing effectiveness of TF-IDF from a statistical significance testing lens. It is our aspiration that these results will open the door to the systematic evaluation of significance testing derived term weighting functions in text analysis applications
Details of TF-reporter experiments.
No full textColors correspond to cellular contexts as indicated in the color legend. Top left: Breakdown of experiments by species. The “mixed” column refers to experiments where the TF and the target genes originated from different species. Top right: Breakdown of experiments by context type. Few TF-reporter experiments were performed using primary tissues or cells. Bottom left: Breakdown of TF-reporter experiments that did or did not investigate the effect of mutating the corresponding cRE sequence. Bottom right: Breakdown of whether the effect of the mutation on the TF-DNA interaction was confirmed experimentally by EMSA. (PDF)</p
The number of unigenes annotated to TF families.
No full textThe horizontal axis shows the name of the TF family. The longitudinal axis shows the number of unigenes classified into different TF families.</p
Who said that? Comparing performance of TF-IDF and fastText to identify authorship of short sentences
No full textAuthorship identification is often applied to large documents, but less so to short, everyday sentences. The ability of identifying who said a short line could provide help to chatbots or personal assistants. This research compares performance of TF-IDF and fastText when identifying authorship of short sentences, by applying these feature extraction techniques to the television series Friends' transcripts. TF-IDF outperforms fastText in every measurement, but its performance is only marginally better than randomly guessing the original character, reaching an accuracy of 28 percent when making a distinction between 6 characters. Accuracy increases linearly at the same rate for both techniques as the minimum word count per sentence set on the test data increases. TF-IDF's confidence remains constant as this limit is set on either the test or training data, whereas fastText's confidence decreases and increases, respectively. Cross-entropy loss, however, remains constant for fastText and decreases for TF-IDF as the minimum word count set on the test data increases.CSE3000 Research ProjectComputer Science and Engineerin
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