Phylogenetic, Genomic and Morphological Investigations of Three Lance Nematode Species (\u3ci\u3eHoplolaimus\u3c/i\u3e spp.)

Lance nematodes (Hoplolaimus spp.) are migratory ecto-endo plant-parasitic. They have been found from a wide range of the world that feed on the roots of a diversity of monocotyledonous and dicotyledonous plants, and have caused a great agricultural damage. Since more taxonomic knowledge and molecular references are demanded for the lance nematode phylogeny and population study, four chapters of lance nematode researches on three species were presented here: (1) A new species, Hoplolaimus smokyensis n. sp., was discovered from a mixed forest sample of maple (Acer sp.), hemlock (Tsuga sp.) and silverbell (Halesia carolina) from the Great Smoky Mountains National Park. It is characterized by possession of a lateral field with four incisures, an excretory pore posterior to the hemizonid, esophageal glands with three nuclei, phasmids anterior and posterior to the vulva, and the epiptygma absent. Phylogenetic analyses based on ribosomal and mitochondrial gene sequences also suggest H. smokyensis n. sp. to be an independent lineage distinct from all other reported Hoplolaimus species. (2) Additional morphological characteristics of Hoplolaimus columbus were described. Photos of its esophageal gland cell nuclei, a H. columbus male and abnormal female tails were presented. (3) The first complete de novo assembly of mitochondrial genome of Hoplolaimus columbus using Whole Genome Amplification and Illumina MiSeq technique was reported as a circularized DNA of 25228bp. The annotation results using two genetic codes were diagnosed and compared. Including H. columbus, phylogenetic relationships, gene content and gene order arrangement of 92 taxa nematodes were analyzed. (4) The phylogenetic informativeness of mitochondrial genes in Nematoda phylum is analyzed with two quantitative methods using mitochondrial genomes of 93 nematode species, including H. columbus and H. galeatus. Results from both methods agree with each other, indicate that the nad5 and nad4 contain higher informativeness than other candidates. Traditional markers like the cox1 and cytb genes contain medium informativeness. The nad4l and nad3 contain the lowest informativeness comparing with other protein-coding genes. Results also indicate that the phylogenetic informativeness is independent of the molecular sequence length of a phylogenetic marker. Concatenated-genes marker could present better phylogenetic informativeness if selected genes are higher informative

MULTI-FEATURE ANALYSIS OF EEG SIGNAL ON SEIZURE PATTERNS AND DEEP NEURAL STRUCTURES FOR PREDICTION OF EPILEPTIC SEIZURES

This work investigates EEG signal processing and seizure prediction based on deep learning architectures. The research includes two major parts. In the first part, we use wavelet decomposition to process the signals and extract signal features from the time-frequency bands. The second part examines the machine learning model and deep learning architecture we have developed for seizure pattern analysis. In our design, the extracted feature maps are processed as image inputs into our convolutional neural network (CNN) model. We proposed a combined CNN-LSTM model to directly process the EEG signals with layers functioning as feature extractors. In cross-validation testing, our CNN feature model can reach an accuracy of 96% and our CNN-LSTM model could reach an accuracy of 98%. We also proposed a matching network architecture that employs two parallel multilayer channels to improve sensitivity

3DFill:Reference-guided Image Inpainting by Self-supervised 3D Image Alignment

Most existing image inpainting algorithms are based on a single view, struggling with large holes or the holes containing complicated scenes. Some reference-guided algorithms fill the hole by referring to another viewpoint image and use 2D image alignment. Due to the camera imaging process, simple 2D transformation is difficult to achieve a satisfactory result. In this paper, we propose 3DFill, a simple and efficient method for reference-guided image inpainting. Given a target image with arbitrary hole regions and a reference image from another viewpoint, the 3DFill first aligns the two images by a two-stage method: 3D projection + 2D transformation, which has better results than 2D image alignment. The 3D projection is an overall alignment between images and the 2D transformation is a local alignment focused on the hole region. The entire process of image alignment is self-supervised. We then fill the hole in the target image with the contents of the aligned image. Finally, we use a conditional generation network to refine the filled image to obtain the inpainting result. 3DFill achieves state-of-the-art performance on image inpainting across a variety of wide view shifts and has a faster inference speed than other inpainting models

Graph-Level Embedding for Time-Evolving Graphs

Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity, ranging from nodes to graphs. While most prior work in this area focuses on node-level representation, limited research has been conducted on graph-level embedding, particularly for dynamic or temporal networks. However, learning low-dimensional graph-level representations for dynamic networks is critical for various downstream graph retrieval tasks such as temporal graph similarity ranking, temporal graph isomorphism, and anomaly detection. In this paper, we present a novel method for temporal graph-level embedding that addresses this gap. Our approach involves constructing a multilayer graph and using a modified random walk with temporal backtracking to generate temporal contexts for the graph's nodes. We then train a "document-level" language model on these contexts to generate graph-level embeddings. We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods. Our experimental results demonstrate the effectiveness of our method in generating graph-level embeddings for dynamic networks.Comment: In Companion Proceedings of the ACM Web Conference 202

The Effect of Air leakage through the Air Cavities of Building Walls on Mold Growth Risks

Mold growth poses a high risk to a large number of existing buildings and their users. Air leakage through the air cavities of the building walls, herein gaps between walls and air conditioner pipes penetrating the walls, may increase the risks of interstitial condensation, mold growth and other moisture-related problems. In order to quantify the mold growth risks due to air leakage through air cavity, an office room in a historical masonry building in Nanjing, China, was selected, and its indoor environment has been studied. Fungi colonization can be seen on the surface of air conditioner pipes in the interior side near air cavity of the wall. Hygrothermometers and thermocouples logged interior and exterior temperature and relative humidity from June 2018 to January 2020. The measured data show that in summer the outdoor humidity remained much higher than that of the room, while the temperature near the air cavity stays lower than those of the other parts in the room. Hot and humid outdoor air may condense on the cold wall surface near an air cavity. A two-dimensional hygrothermal simulation was made. Air leakage through the air cavities of walls proved to be a crucial factor for mold growth

Online near-infrared analysis coupled with MWPLS and SiPLS models for the multi-ingredient and multi-phase extraction of licorice (Gancao)

Additional file 1. Table S1. The sampling intervals in different extraction phases. Table S2. The HPLC results of different indicators. Table S3. The evaluation parameters of PLS and SiPLS models

You Watch, You Give, and You Engage: A Study of Live Streaming Practices in China

Despite gaining traction in North America, live streaming has not reached the popularity it has in China, where livestreaming has a tremendous impact on the social behaviors of users. To better understand this socio-technological phenomenon, we conducted a mixed methods study of live streaming practices in China. We present the results of an online survey of 527 live streaming users, focusing on their broadcasting or viewing practices and the experiences they find most engaging. We also interviewed 14 active users to explore their motivations and experiences. Our data revealed the different categories of content that was broadcasted and how varying aspects of this content engaged viewers. We also gained insight into the role reward systems and fan group-chat play in engaging users, while also finding evidence that both viewers and streamers desire deeper channels and mechanisms for interaction in addition to the commenting, gifting, and fan groups that are available today.Comment: Published at ACM CHI Conference on Human Factors in Computing Systems (CHI 2018). Please cite the CHI versio

An Integration Method of Bursting Strain Energy and Seismic Velocity Tomography for Coal Burst Hazard Assessment

AbstractThe occurrence of coal burst in underground coal mines is complex, abrupt, and diverse, and the evaluation and prediction of coal burst hazard is the premise of effective prevention and control of coal burst. In this study, a coal burst carrier system model under the synergistic action of roof, coal seams, and floor was established, and the evolution of coal burst in underground coal mines was discussed based on the stress-vibration-energy coupling principle. On this basis, an integration method of bursting strain energy and seismic velocity tomography for coal burst assessment was proposed. With the deep and complex panel in a mine as the research object, the coal burst risk of the panel during excavation was evaluated in time and space domains, respectively. Results showed that the bursting strain energy and the active seismic velocity tomography technology can accurately identify both the positive anomalies and the negative anomalies of stress field and energy field in the mining period. Moreover, the method can not only evaluate the coal burst risk of the panel in the temporal domain but also predict the area with potential strong seismic events in the spatial domain. The research conclusions can accurately illustrate the whole complex evolution process of coal burst in underground coal mines