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In 1996, Cromwell and Nutt [7] found an upper bound on the arc index which is related to the minimal crossing number and conjectured that the upper bound achieves the lowest possible index for alternating links.CONJECTURE. Let L be any prime link. Then α(L) [les ] c(L)+2. Moreover this inequality is strict if and only if L is not alternating.In this paper, we define a new diagram, called a wheel diagram, of a link and use it to prove this conjecture.

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BackgroundBiomedical named entity recognition (Bio-NER) is a fundamental task in handling biomedical text terms, such as RNA, protein, cell type, cell line, and DNA. Bio-NER is one of the most elementary and core tasks in biomedical knowledge discovery from texts. The system described here is developed by using the BioNLP/NLPBA 2004 shared task. Experiments are conducted on a training and evaluation set provided by the task organizers.ResultsOur results show that, compared with a baseline having a 70.09% F1 score, the RNN Jordan- and Elman-type algorithms have F1 scores of approximately 60.53% and 58.80%, respectively. When we use CRF as a machine learning algorithm, CCA, GloVe, and Word2Vec have F1 scores of 72.73%, 72.74%, and 72.82%, respectively.ConclusionsBy using the word embedding constructed through the unsupervised learning, the time and cost required to construct the learning data can be saved.

BackgroundIn general, the image analysis of nucleic acid for detecting DNA is dependent on the gel documentation system. These experiments may deal with harmful staining agents and are time consuming. To address these issues, real-time polymerase chain reaction (PCR) devices have been developed. The advantages of real-time PCR are its capabilities for real-time diagnosis, improved sensitivity, and digitization of measurement results. However, real-time PCR equipment is still too bulky and expensive for use in small hospitals and laboratories.MethodsThis paper describes an evaluation-independent real-time PCR system that differs from conventional systems in that it uses a side-illumination optical detection system and a temperature adjustment coefficient for DNA detection. The overall configuration of the evaluation-independent system includes the PCR chip and system hardware and software. The use of the side-illumination method for detection enables the system size to be reduced compared to systems using a typical illumination method. Furthermore, the results of a PCR test are strongly affected by the reaction temperature. Thus, extremely precise control of the temperature of the reaction is needed to obtain accurate results and good reliability. We derived a temperature compensation coefficient that allows us to compensate for the differences between the measured temperature of the negative temperature coefficient (NTC) thermistor sensor and the real temperature of the thermocouple.ResultsApplying the temperature compensation coefficient parameter using the NTC thermistor and using the side-illumination method resulted in an increase in the initial sensor value. The occurrence of the DNA section amplification decreased to 22 cycles from 24 cycles.ConclusionsThe proposed system showed comparable performance to that of an existing real-time PCR, even with the use of simpler and smaller optical devices.

Semantic similarity evaluation is used in various fields such as question-and-answering and plagiarism testing, and many studies have been conducted into this problem. In previous studies using neural networks to evaluate semantic similarity, similarity has been measured using global information of sentence pairs. However, since sentences do not only have one meaning but a variety of meanings, using only global information can have a negative effect on performance improvement. Therefore, in this study, we propose a model that uses global information and local information simultaneously to evaluate the semantic similarity of sentence pairs. The proposed model can adjust whether to focus more on global information or local information through a weight parameter. As a result of the experiment, the proposed model can show that the accuracy is higher than existing models that use only global information.