Constructing Cooking Ontology for Live Streams

We build a cooking domain knowledge by using an ontology schema that reflects natural language processing and enhances ontology instances with semantic query. Our research helps audiences to better understand live streaming, especially when they just switch to a show. The practical contribution of our research is to use cooking ontology, so we may map clips of cooking live stream video and instructions of recipes. The architecture of our study presents three sections: ontology construction, ontology enhancement, and mapping cooking video to cooking ontology. Also, our preliminary evaluations consist of three hierarchies—nodes, ordered-pairs, and 3-tuples—that we use to referee (1) ontology enhancement performance for our first experiment evaluation and (2) the accuracy ratio of mapping between video clips and cooking ontology for our second experiment evaluation. Our results indicate that ontology enhancement is effective and heightens accuracy ratios on matching pairs with cooking ontology and video clips

The Research on the Detection of Noteworthy Symptom Descriptions

The advance of mobile devices and communication technologies enable patients to communicate with their doctors in a more convenient way. We have developed an App that allows patients to record their symptoms and submit them to their doctors. Physicians can keep track of patients’ conditions by looking at the self-report messages. Nevertheless, physicians are usually busy and may be overwhelmed by the large amount of incoming messages. As a result, critical messages may not receive immediate attentions, and patient care is compromised. It is imperative to identify the messages that require physicians’ attention, called noteworthy messages. In this research, we propose an approach that applies text-mining technologies to identify medical symptoms conveyed in the messages and their associated sentiment orientation, as well as other factors. Noteworthy messages are subsequently characterized by symptom sentiment and symptom change features. We then construct a prediction model to identify messages that are noteworthy to the physicians. We show from our experiments using data collected from a teaching hospital in Taiwan that the different features have different degrees of impact on the performance of the prediction model, and our proposed approach can effectively identify noteworthy messages

Identification and Characterization of microRNAs from Peanut (Arachis hypogaea L.) by High-Throughput Sequencing

BACKGROUND: MicroRNAs (miRNAs) are noncoding RNAs of approximately 21 nt that regulate gene expression in plants post-transcriptionally by endonucleolytic cleavage or translational inhibition. miRNAs play essential roles in numerous developmental and physiological processes and many of them are conserved across species. Extensive studies of miRNAs have been done in a few model plants; however, less is known about the diversity of these regulatory RNAs in peanut (Arachis hypogaea L.), one of the most important oilseed crops cultivated worldwide. RESULTS: A library of small RNA from peanut was constructed for deep sequencing. In addition to 126 known miRNAs from 33 families, 25 novel peanut miRNAs were identified. The miRNA* sequences of four novel miRNAs were discovered, providing additional evidence for the existence of miRNAs. Twenty of the novel miRNAs were considered to be species-specific because no homolog has been found for other plant species. qRT-PCR was used to analyze the expression of seven miRNAs in different tissues and in seed at different developmental stages and some showed tissue- and/or growth stage-specific expression. Furthermore, potential targets of these putative miRNAs were predicted on the basis of the sequence homology search. CONCLUSIONS: We have identified large numbers of miRNAs and their related target genes through deep sequencing of a small RNA library. This study of the identification and characterization of miRNAs in peanut can initiate further study on peanut miRNA regulation mechanisms, and help toward a greater understanding of the important roles of miRNAs in peanut

Identity-Aware Hand Mesh Estimation and Personalization from RGB Images

Reconstructing 3D hand meshes from monocular RGB images has attracted increasing amount of attention due to its enormous potential applications in the field of AR/VR. Most state-of-the-art methods attempt to tackle this task in an anonymous manner. Specifically, the identity of the subject is ignored even though it is practically available in real applications where the user is unchanged in a continuous recording session. In this paper, we propose an identity-aware hand mesh estimation model, which can incorporate the identity information represented by the intrinsic shape parameters of the subject. We demonstrate the importance of the identity information by comparing the proposed identity-aware model to a baseline which treats subject anonymously. Furthermore, to handle the use case where the test subject is unseen, we propose a novel personalization pipeline to calibrate the intrinsic shape parameters using only a few unlabeled RGB images of the subject. Experiments on two large scale public datasets validate the state-of-the-art performance of our proposed method.Comment: ECCV 2022. Github https://github.com/deyingk/PersonalizedHandMeshEstimatio

Design and Implementation of a Novel Directional Coupler for UHF RFID Reader

The directional coupler is applied to isolating RX from TX because of low cost and simplicity compared to the circulator in the radio-frequency identification (RFID) reader. Because of unequal phase velocity between odd and even mode, the drawback of the traditional microstrip directional coupler is poor isolation. In this paper, to obtain a good isolation between RX and TX, a novel directional coupler is proposed to be applied to the UHF RFID system with a single antenna. Measurement result shows that the proposed directional coupler possesses a good isolation of -35dB in operating frequency band

Efficient COI barcoding using high throughput single-end 400 bp sequencing

Background Over the last decade, the rapid development of high-throughput sequencing platforms has accelerated species description and assisted morphological classification through DNA barcoding. However, the current high-throughput DNA barcoding methods cannot obtain full-length barcode sequences due to read length limitations (e.g. a maximum read length of 300 bp for the Illumina’s MiSeq system), or are hindered by a relatively high cost or low sequencing output (e.g. a maximum number of eight million reads per cell for the PacBio’s SEQUEL II system). Results Pooled cytochrome c oxidase subunit I (COI) barcodes from individual specimens were sequenced on the MGISEQ-2000 platform using the single-end 400 bp (SE400) module. We present a bioinformatic pipeline, HIFI-SE, that takes reads generated from the 5′ and 3′ ends of the COI barcode region and assembles them into full-length barcodes. HIFI-SE is written in Python and includes four function modules of filter, assign, assembly and taxonomy. We applied the HIFI-SE to a set of 845 samples (30 marine invertebrates, 815 insects) and delivered a total of 747 fully assembled COI barcodes as well as 70 Wolbachia and fungi symbionts. Compared to their corresponding Sanger sequences (72 sequences available), nearly all samples (71/72) were correctly and accurately assembled, including 46 samples that had a similarity score of 100% and 25 of ca. 99%. Conclusions The HIFI-SE pipeline represents an efficient way to produce standard full-length barcodes, while the reasonable cost and high sensitivity of our method can contribute considerably more DNA barcodes under the same budget. Our method thereby advances DNA-based species identification from diverse ecosystems and increases the number of relevant applications

Efficient \u3ci\u3eCOI\u3c/i\u3e Barcoding Using High Throughput Single-End 400 bp Sequencing

Background Over the last decade, the rapid development of high-throughput sequencing platforms has accelerated species description and assisted morphological classification through DNA barcoding. However, the current highthroughput DNA barcoding methods cannot obtain full-length barcode sequences due to read length limitations (for example, a maximum read length of 300 bp for the Illumina’s MiSeq system), or are hindered by a relatively high cost or low sequencing output (e.g. a maximum number of eight million reads per cell for the PacBio’s SEQUEL II system). Results Pooled cytochrome c oxidase subunit I (COI) barcodes from individual specimens were sequenced on the MGISEQ-2000 platform using the single-end 400 bp (SE400) module. We present a bioinformatic pipeline, HIFI-SE, that takes reads generated from the 5′ and 3′ ends of the COI barcode region and assembles them into full-length barcodes. HIFI-SE is written in Python and includes four function modules of filter, assign, assembly, and taxonomy. We applied the HIFI-SE to a set of 845 samples (30 marine invertebrates, 815 insects) and delivered a total of 747 fully assembled COI barcodes as well as 70 Wolbachia and fungi symbionts. Compared to their corresponding Sanger sequences (72 sequences available), nearly all samples (71/72) were correctly and accurately assembled, including 46 samples that had a similarity score of 100% and 25 of ca. 99%. Conclusions The HIFI-SE pipeline represents an efficient way to produce standard full-length barcodes, while the reasonable cost and high sensitivity of our method can contribute considerably more DNA barcodes under the same budget. Our method thereby advances DNA-based species identification from diverse ecosystems and increases the number of relevant applications