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

LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation

UNet and its variants have been widely used in medical image segmentation. However, these models, especially those based on Transformer architectures, pose challenges due to their large number of parameters and computational loads, making them unsuitable for mobile health applications. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as competitive alternatives to CNN and Transformer architectures. Building upon this, we employ Mamba as a lightweight substitute for CNN and Transformer within UNet, aiming at tackling challenges stemming from computational resource limitations in real medical settings. To this end, we introduce the Lightweight Mamba UNet (LightM-UNet) that integrates Mamba and UNet in a lightweight framework. Specifically, LightM-UNet leverages the Residual Vision Mamba Layer in a pure Mamba fashion to extract deep semantic features and model long-range spatial dependencies, with linear computational complexity. Extensive experiments conducted on two real-world 2D/3D datasets demonstrate that LightM-UNet surpasses existing state-of-the-art literature. Notably, when compared to the renowned nnU-Net, LightM-UNet achieves superior segmentation performance while drastically reducing parameter and computation costs by 116x and 21x, respectively. This highlights the potential of Mamba in facilitating model lightweighting. Our code implementation is publicly available at https://github.com/MrBlankness/LightM-UNet

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

Latte: Latent Diffusion Transformer for Video Generation

We propose a novel Latent Diffusion Transformer, namely Latte, for video generation. Latte first extracts spatio-temporal tokens from input videos and then adopts a series of Transformer blocks to model video distribution in the latent space. In order to model a substantial number of tokens extracted from videos, four efficient variants are introduced from the perspective of decomposing the spatial and temporal dimensions of input videos. To improve the quality of generated videos, we determine the best practices of Latte through rigorous experimental analysis, including video clip patch embedding, model variants, timestep-class information injection, temporal positional embedding, and learning strategies. Our comprehensive evaluation demonstrates that Latte achieves state-of-the-art performance across four standard video generation datasets, i.e., FaceForensics, SkyTimelapse, UCF101, and Taichi-HD. In addition, we extend Latte to text-to-video generation (T2V) task, where Latte achieves comparable results compared to recent T2V models. We strongly believe that Latte provides valuable insights for future research on incorporating Transformers into diffusion models for video generation.Comment: Project page: https://maxin-cn.github.io/latte_projec

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