Upregulation of NUAK2: A novel prognostic marker in breast cancer

Background. Breast cancer is the most commonly diagnosed neoplasm in women worldwide. New molecular biomarkers and effective prognostic models are being developed. This study aimed to investigate the clinical and prognostic significance of NUAK2 expression in patients with breast cancer. Methods. The expression of NUAK 2 was examined in breast cancer cells and tissues by real-time PCR, western blotting, and immunohistochemical staining. CCK-8 and colony formation assays were performed to verify the effect of NUAK2 on the proliferation and tumor progression of breast cancer cells. A tumor formation assay in nude mice was performed to analyze the effect of NUAK2 on the tumorigenicity of breast cancer cells. Results. The expression of NUAK2 in breast cancer tissues was higher than that in paracarcinoma and normal breast tissues. The overall survival of patients with high NUAK2 expression was significantly lower than that of patients with low NUAK2 expression. Multivariate analyses indicated that NUAK2 was an independent prognostic indicator of survival in breast cancer. In vitro experiments demonstrated that knocking down NUAK2 in breast cancer cells inhibited cell proliferation and tumor-forming ability, and overexpression of NUAK2 showed the opposite effects. NUAK2 overexpression promoted the tumorigenicity of breast cancer cells in vivo. Conclusion. These findings suggest that NUAK2 is involved in breast cancer development and progression. NUAK2 may be a valuable prognostic indicator in patients with breast cancer

Histone modifications in embryo implantation and placentation: insights from mouse models

Embryo implantation and placentation play pivotal roles in pregnancy by facilitating crucial maternal-fetal interactions. These dynamic processes involve significant alterations in gene expression profiles within the endometrium and trophoblast lineages. Epigenetics regulatory mechanisms, such as DNA methylation, histone modification, chromatin remodeling, and microRNA expression, act as regulatory switches to modulate gene activity, and have been implicated in establishing a successful pregnancy. Exploring the alterations in these epigenetic modifications can provide valuable insights for the development of therapeutic strategies targeting complications related to pregnancy. However, our current understanding of these mechanisms during key gestational stages remains incomplete. This review focuses on recent advancements in the study of histone modifications during embryo implantation and placentation, while also highlighting future research directions in this field

Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network

The expectation to deploy a universal neural network for speech enhancement, with the aim of improving noise robustness across diverse speech processing tasks, faces challenges due to the existing lack of awareness within static speech enhancement frameworks regarding the expected speech in downstream modules. These limitations impede the effectiveness of static speech enhancement approaches in achieving optimal performance for a range of speech processing tasks, thereby challenging the notion of universal applicability. The fundamental issue in achieving universal speech enhancement lies in effectively informing the speech enhancement module about the features of downstream modules. In this study, we present a novel weighting prediction approach, which explicitly learns the task relationships from downstream training information to address the core challenge of universal speech enhancement. We found the role of deciding whether to employ data augmentation techniques as crucial downstream training information. This decision significantly impacts the expected speech and the performance of the speech enhancement module. Moreover, we introduce a novel speech enhancement network, the Plugin Speech Enhancement (Plugin-SE). The Plugin-SE is a dynamic neural network that includes the speech enhancement module, gate module, and weight prediction module. Experimental results demonstrate that the proposed Plugin-SE approach is competitive or superior to other joint training methods across various downstream tasks

YOLO-SCL: a lightweight detection model for citrus psyllid based on spatial channel interaction

Efficient and accurate detection and providing early warning for citrus psyllids is crucial as they are the primary vector of citrus huanglongbing. In this study, we created a dataset comprising images of citrus psyllids in natural environments and proposed a lightweight detection model based on the spatial channel interaction. First, the YOLO-SCL model was based on the YOLOv5s architecture, which uses an efficient channel attention module to perform local channel attention on the inputs in the recursive gated convolutional modules to achieve a combination of global spatial and local channel interactions, improving the model’s ability to express the features of the critical regions of small targets. Second, the lightweight design of the 21st layer C3 module in the neck network of the YOLO-SCL model and the small target feature information were retained to the maximum extent by deleting the two convolutional layers, whereas the number of parameters was reduced to improve the detection accuracy of the model. Third, with the detection accuracy of the YOLO-SCL model as the objective function, the black widow optimization algorithm was used to optimize the hyperparameters of the YOLO-SCL model, and the iterative mechanism of swarm intelligence was used to further improve the model performance. The experimental results showed that the YOLO-SCL model achieved a [email protected] of 97.07% for citrus psyllids, which was 1.18% higher than that achieved using conventional YOLOv5s model. Meanwhile, the number of parameters and computation amount of the YOLO-SCL model are 6.92 M and 15.5 GFlops, respectively, which are 14.25% and 2.52% lower than those of the conventional YOLOv5s model. In addition, after using the black widow optimization algorithm to optimize the hyperparameters, the [email protected] of the YOLO-SCL model for citrus psyllid improved to 97.18%, making it more suitable for the natural environments in which citrus psyllids are to be detected. The experimental results showed that the YOLO-SCL model has good detection accuracy for citrus psyllids, and the model was ported to the Jetson AGX Xavier edge computing platform, with an average processing time of 38.8 ms for a single-frame image and a power consumption of 16.85 W. This study provides a new technological solution for the safety of citrus production

RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning

We study unsupervised video representation learning that seeks to learn both motion and appearance features from unlabeled video only, which can be reused for downstream tasks such as action recognition. This task, however, is extremely challenging due to 1) the highly complex spatial-temporal information in videos; and 2) the lack of labeled data for training. Unlike the representation learning for static images, it is difficult to construct a suitable self-supervised task to well model both motion and appearance features. More recently, several attempts have been made to learn video representation through video playback speed prediction. However, it is non-trivial to obtain precise speed labels for the videos. More critically, the learnt models may tend to focus on motion pattern and thus may not learn appearance features well. In this paper, we observe that the relative playback speed is more consistent with motion pattern, and thus provide more effective and stable supervision for representation learning. Therefore, we propose a new way to perceive the playback speed and exploit the relative speed between two video clips as labels. In this way, we are able to well perceive speed and learn better motion features. Moreover, to ensure the learning of appearance features, we further propose an appearance-focused task, where we enforce the model to perceive the appearance difference between two video clips. We show that optimizing the two tasks jointly consistently improves the performance on two downstream tasks, namely action recognition and video retrieval. Remarkably, for action recognition on UCF101 dataset, we achieve 93.7% accuracy without the use of labeled data for pre-training, which outperforms the ImageNet supervised pre-trained model. Code and pre-trained models can be found at https://github.com/PeihaoChen/RSPNet.Comment: Accepted by AAAI-2021. Code and pre-trained models can be found at https://github.com/PeihaoChen/RSPNe

Oriented Three-Dimensional Magnetic Biskyrmion in MnNiGa Bulk Crystals

A biskyrmion consists of two bound, topologically stable skyrmion spin textures. These coffee-bean-shaped objects have been observed in real-space in thin plates using Lorentz transmission electron microscopy (LTEM). From LTEM imaging alone, it is not clear whether biskyrmions are surface-confined objects, or, analogously to skyrmions in non-centrosymmetric helimagnets, three-dimensional tube-like structures in bulk sample. Here, we investigate the biskyrmion form factor in single- and polycrystalline MnNiGa samples using small angle neutron scattering (SANS). We find that biskyrmions are not long-range ordered, not even in single-crystals. Surprisingly all of the disordered biskyrmions have their in-plane symmetry axis aligned along certain directions, governed by the magnetocrystalline anisotropy. This anisotropic nature of biskyrmions may be further exploited to encode information

Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development

Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making. However, their application in 3D lane detection for effective driving environment perception is hindered by the lack of comprehensive LiDAR datasets. The sparse nature of LiDAR point cloud data prevents an efficient manual annotation process. To solve this problem, we present LiSV-3DLane, a large-scale 3D lane dataset that comprises 20k frames of surround-view LiDAR point clouds with enriched semantic annotation. Unlike existing datasets confined to a frontal perspective, LiSV-3DLane provides a full 360-degree spatial panorama around the ego vehicle, capturing complex lane patterns in both urban and highway environments. We leverage the geometric traits of lane lines and the intrinsic spatial attributes of LiDAR data to design a simple yet effective automatic annotation pipeline for generating finer lane labels. To propel future research, we propose a novel LiDAR-based 3D lane detection model, LiLaDet, incorporating the spatial geometry learning of the LiDAR point cloud into Bird's Eye View (BEV) based lane identification. Experimental results indicate that LiLaDet outperforms existing camera- and LiDAR-based approaches in the 3D lane detection task on the K-Lane dataset and our LiSV-3DLane.Comment: 7 pages, 6 figure

DiffusionGPT: LLM-Driven Text-to-Image Generation System

Diffusion models have opened up new avenues for the field of image generation, resulting in the proliferation of high-quality models shared on open-source platforms. However, a major challenge persists in current text-to-image systems are often unable to handle diverse inputs, or are limited to single model results. Current unified attempts often fall into two orthogonal aspects: i) parse Diverse Prompts in input stage; ii) activate expert model to output. To combine the best of both worlds, we propose DiffusionGPT, which leverages Large Language Models (LLM) to offer a unified generation system capable of seamlessly accommodating various types of prompts and integrating domain-expert models. DiffusionGPT constructs domain-specific Trees for various generative models based on prior knowledge. When provided with an input, the LLM parses the prompt and employs the Trees-of-Thought to guide the selection of an appropriate model, thereby relaxing input constraints and ensuring exceptional performance across diverse domains. Moreover, we introduce Advantage Databases, where the Tree-of-Thought is enriched with human feedback, aligning the model selection process with human preferences. Through extensive experiments and comparisons, we demonstrate the effectiveness of DiffusionGPT, showcasing its potential for pushing the boundaries of image synthesis in diverse domains