Diversity-Robust Acoustic Feature Signatures Based on Multiscale Fractal Dimension for Similarity Search of Environmental Sounds

This paper proposes new acoustic feature signatures based on the multiscale fractal dimension (MFD), which are robust against the diversity of environmental sounds, for the content-based similarity search. The diversity of sound sources and acoustic compositions is a typical feature of environmental sounds. Several acoustic features have been proposed for environmental sounds. Among them is the widely-used Mel-Frequency Cepstral Coefficients (MFCCs), which describes frequency-domain features. However, in addition to these features in the frequency domain, environmental sounds have other important features in the time domain with various time scales. In our previous paper, we proposed enhanced multiscale fractal dimension signature (EMFD) for environmental sounds. This paper extends EMFD by using the kernel density estimation method, which results in better performance of the similarity search tasks. Furthermore, it newly proposes another acoustic feature signature based on MFD, namely very-long-range multiscale fractal dimension signature (MFD-VL). The MFD-VL signature describes several features of the time-varying envelope for long periods of time. The MFD-VL signature has stability and robustness against background noise and small fluctuations in the parameters of sound sources, which are produced in field recordings. We discuss the effectiveness of these signatures in the similarity sound search by comparing with acoustic features proposed in the DCASE 2018 challenges. Due to the unique descriptiveness of our proposed signatures, we confirmed the signatures are effective when they are used with other acoustic features.Comment: 15 pages, 14 figure

Target-selective homologous recombination cloning for high-throughput generation of monoclonal antibodies from single plasma cells

<p>Abstract</p> <p>Background</p> <p>Molecular cloning of functional immunoglobulin genes from single plasma cells is one of the most promising technologies for the rapid development of monoclonal antibody drugs. However, the proper insertion of PCR-amplified immunoglobulin genes into expression vectors remains an obstacle to the high-throughput production of recombinant monoclonal antibodies.</p> <p>Results</p> <p>We developed a single-step cloning method, target-selective homologous recombination (TS-HR), in which PCR-amplified immunoglobulin variable genes were selectively inserted into vectors, even in the presence of nonspecifically amplified DNA. TS-HR utilizes Red/ET-mediated homologous recombination with a target-selective vector (TS-vector) with unique homology arms on its termini. Using TS-HR, immunoglobulin variable genes were cloned directly into expression vectors by co-transforming unpurified PCR products and the TS-vector into <it>E. coli</it>. Furthermore, the high cloning specificity of TS-HR allowed plasmids to be extracted from pools of transformed bacteria without screening single colonies for correct clones. We present a one-week protocol for the production of recombinant mouse monoclonal antibodies from large numbers of single plasma cells.</p> <p>Conclusion</p> <p>The time requirements and limitations of traditional cloning procedures for the production of recombinant immunoglobulins have been significantly reduced with the development of the TS-HR cloning technique.</p

Indication and benefit of upfront hematopoietic stem cell transplantation for T-cell lymphoblastic lymphoma in the era of ALL-type induction therapies

Since the introduction of leukemia-type induction therapies for T-cell lymphoblastic lymphoma (T-LBL), improvements in the long-term outcomes of T-LBL have been reported. However, indications for and the appropriate timing of hematopoietic stem cell transplantation (HSCT) have not yet been established. Therefore, we performed a multicenter retrospective cohort study of patients with T-LBL treated using leukemia-type initial therapies to compare the outcomes after HSCT at different disease stages. We enrolled 21 patients with T-LBL from a total of 11 centers, and all patients received hyper-CVAD as a leukemia-type initial regimen. HSCT was performed during the CR1/PR1 (standard disease) stage in 11 patients, while it was completed at a later or non-remission (advanced disease) stage in 10 patients. Following HSCT, the overall survival rate was significantly greater in standard disease than in advanced-disease patients (79.5% vs. 30.0% at 5 years; hazard ratio (HR) 5.97; p = 0.03), with trend to the lower incidence of relapse in the former group (27.3% vs. 60.0% at 5 years; HR 2.29; p = 0.19). A prognostic difference was not detected between cases treated with allogeneic and autologous HSCTs. Our study suggests that frontline HSCT may be a feasible treatment option for T-LBL, even in the era of leukemia-type initial therapy

The CHEMDNER corpus of chemicals and drugs and its annotation principles

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus

Knowledge exploratory project for nanodevice design and manufacturing

We are developing a framework for knowledge creation in nanodevice development, based on collaboration between nanodevice engineers and computer science researchers. Development of nanodevices requires a variety of knowledge; some of this knowledge is tacit, based on the user's experience. Therefore, it is difficult to become a good engineer in this development process. We propose the concept of "Evidence-based experiment planning" and develop a process for supporting experiment planning in nanodevice development. This system applies knowledge discovery techniques to records of previous experiments to extract experienced engineers' tacit knowledge

Framework for automatic information extraction from research papers on nanocrystal devices

To support nanocrystal device development, we have been working on a computational framework to utilize information in research papers on nanocrystal devices. We developed an annotated corpus called "NaDev" (Nanocrystal Device Development) for this purpose. We also proposed an automatic information extraction system called "NaDevEx" (Nanocrystal Device Automatic Information Extraction Framework). NaDevEx aims at extracting information from research papers on nanocrystal devices using the NaDev corpus and machine-learning techniques. However, the characteristics of NaDevEx were not examined in detail. In this paper, we conduct system evaluation experiments for NaDevEx using the NaDev corpus. We discuss three main issues: system performance, compared with human annotators; the effect of paper type (synthesis or characterization) on system performance; and the effects of domain knowledge features (e.g., a chemical named entity recognition system and list of names of physical quantities) on system performance. We found that overall system performance was 89% in precision and 69% in recall. If we consider identification of terms that intersect with correct terms for the same information category as the correct identification, i.e., loose agreement (in many cases, we can find that appropriate head nouns such as temperature or pressure loosely match between two terms), the overall performance is 95% in precision and 74% in recall. The system performance is almost comparable with results of human annotators for information categories with rich domain knowledge information (source material). However, for other information categories, given the relatively large number of terms that exist only in one paper, recall of individual information categories is not high (39-73%); however, precision is better (75-97%). The average performance for synthesis papers is better than that for characterization papers because of the lack of training examples for characterization papers. Based on these results, we discuss future research plans for improving the performance of the system

Drug delivery application of extracellular vesicles; insight into production, drug loading, targeting, and pharmacokinetics

Extracellular vesicles (EVs) are secreted from any types of cells and shuttle between donor cells and recipient cells. Since EVs deliver their cargos such as proteins, nucleic acids, and other molecules for intercellular communication, they are considered as novel mode of drug delivery vesicles. EVs possess advantages such as inherent targeting ability and non-toxicity over conventional nanocarriers. Much efforts have so far been made for the application of EVs as a drug delivery carrier, however, basic techniques, such as mass-scale production, drug loading, and engineering of EVs are still limited. In this review, we summarize following four points. First, recent progress on the production method for EVs is described. Second, current techniques of drug loading methods are summarized. Third, targeting approach to specifically deliver cargo molecules for diseased sites by engineered EVs is discussed. Lastly, strategies to control pharmacokinetics and improve biodistribution are discussed