One-Year-Old Precocious Chinese Mitten Crab Identification Algorithm Based on Task Alignment.

The cultivation of the Chinese mitten crab (Eriocheir sinensis) is an important component of China's aquaculture industry and also a field of concern worldwide. It focuses on the selection of high-quality, disease-free juvenile crabs. However, the early maturity rate of more than 18.2% and the mortality rate of more than 60% make it difficult to select suitable juveniles for adult culture. The juveniles exhibit subtle distinguishing features, and the methods for differentiating between sexes vary significantly; without training from professional breeders, it is challenging for laypersons to identify and select the appropriate juveniles. Therefore, we propose a task-aligned detection algorithm for identifying one-year-old precocious Chinese mitten crabs, named R-TNET. Initially, the required images were obtained by capturing key frames, and then they were annotated and preprocessed by professionals to build a training dataset. Subsequently, the ResNeXt network was selected as the backbone feature extraction network, with Convolutional Block Attention Modules (CBAMs) and a Deformable Convolution Network (DCN) embedded in its residual blocks to enhance its capability to extract complex features. Adaptive spatial feature fusion (ASFF) was then integrated into the feature fusion network to preserve the detailed features of small targets such as one-year-old precocious Chinese mitten crab juveniles. Finally, based on the detection head proposed by task-aligned one-stage object detection, the parameters of its anchor alignment metric were adjusted to detect, locate, and classify the crab juveniles. The experimental results showed that this method achieves a mean average precision (mAP) of 88.78% and an F1-score of 97.89%. This exceeded the best-performing mainstream object detection algorithm, YOLOv7, by 4.17% in mAP and 1.77% in the F1-score. Ultimately, in practical application scenarios, the algorithm effectively identified one-year-old precocious Chinese mitten crabs, providing technical support for the automated selection of high-quality crab juveniles in the cultivation process, thereby promoting the rapid development of aquaculture and agricultural intelligence in China.

Research on Fatigue Driving Detection Technology Based on CA-ACGAN.

Driver fatigue represents a significant peril to global traffic safety, necessitating the advancement of potent fatigue monitoring methodologies to bolster road safety. This research introduces a conditional generative adversarial network with a classification head that integrates convolutional and attention mechanisms (CA-ACGAN) designed for the precise identification of fatigue driving states through the analysis of electroencephalography (EEG) signals. First, this study constructed a 4D feature data model capable of mirroring drivers' fatigue state, meticulously analyzing the EEG signals' frequency, spatial, and temporal dimensions. Following this, we present the CA-ACGAN framework, a novel integration of attention schemes, the bottleneck residual block, and the Transformer element. This integration was designed to refine the processing of EEG signals significantly. In utilizing a conditional generative adversarial network equipped with a classification header, the framework aims to distinguish fatigue states effectively. Moreover, it addresses the scarcity of authentic data through the generation of superior-quality synthetic data. Empirical outcomes illustrate that the CA-ACGAN model surpasses various extant methods in the fatigue detection endeavor on the SEED-VIG public dataset. Moreover, juxtaposed with leading-edge GAN models, our model exhibits an efficacy in in producing high-quality data that is clearly superior. This investigation confirms the CA-ACGAN model's utility in fatigue driving identification and suggests fresh perspectives for deep learning applications in time series data generation and processing.

Downregulation of circETS1 disrupts Th17/Treg homeostasis by inhibiting FOXP3 transcription: A new potential biomarker in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease associated with an imbalance of T helper 17 (Th17) to regulatory T cells (Tregs). However, the underlying mechanism remains unclear. Increasing evidence suggests that circular RNAs play a crucial role in SLE. Although circETS1 was discovered 30 years ago, detailed exploration of its functions remains limited. In this study, we measured the expression levels of circETS1 in peripheral blood mononuclear cells (PBMCs) and CD4+ T cells of patients with SLE by quantitative polymerase chain reaction. The impact of circETS1 expression on the Th17/Treg balance and underlying mechanism were evaluated using double-luciferase reporter, gain-/loss-of-function, and rescue assays. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic value of circETS1. Both circETS1 and FOXP3 expression were downregulated in the PBMCs and CD4+ T cells of patients with SLE (n = 28) compared with those in the cells of healthy controls (n = 20). Mechanistically, we found that circETS1 can bind directly to the microRNA miR-1205, acting as a sponge to upregulate the transcription of FOXP3, thereby maintaining the Th17/Treg balance. Notably, ROC analysis showed that the expression level of circETS1 in PBMCs had an area under the curve of 0.873 (95% confidence interval: 0.771-0.976; p < .001) for diagnosing SLE, with a sensitivity of 80.00% and a specificity of 89.29%. Finally, we found negative correlations between the level of circETS1 in PBMCs and disease severity (according to the Systemic Lupus Erythematosus Disease Activity Index) in patients with SLE (r = -0.7712, 95% CI: -0.8910 to -0.5509; p < .001). The imbalance in Th17/Treg cells in SLE may be attributed, in part, to the circETS1/miR-1205/FOXP3 pathway. CircETS1 has potential to serve as a valuable biomarker for the diagnosis and evaluation of SLE.

Tracking by segmentation with future motion estimation applied to person-following robots.

Person-following is a crucial capability for service robots, and the employment of vision technology is a leading trend in building environmental understanding. While most existing methodologies rely on a tracking-by-detection strategy, which necessitates extensive datasets for training and yet remains susceptible to environmental noise, we propose a novel approach: real-time tracking-by-segmentation with a future motion estimation framework. This framework facilitates pixel-level tracking of a target individual and predicts their future motion. Our strategy leverages a single-shot segmentation tracking neural network for precise foreground segmentation to track the target, overcoming the limitations of using a rectangular region of interest (ROI). Here we clarify that, while the ROI provides a broad context, the segmentation within this bounding box offers a detailed and more accurate position of the human subject. To further improve our approach, a classification-lock pre-trained layer is utilized to form a constraint that curbs feature outliers originating from the person being tracked. A discriminative correlation filter estimates the potential target region in the scene to prevent foreground misrecognition, while a motion estimation neural network anticipates the target's future motion for use in the control module. We validated our proposed methodology using the VOT, LaSot, YouTube-VOS, and Davis tracking datasets, demonstrating its effectiveness. Notably, our framework supports long-term person-following tasks in indoor environments, showing promise for practical implementation in service robots.

A gene pathway enrichment method based on improved TF-IDF algorithm.

Gene pathway enrichment analysis is a widely used method to analyze whether a gene set is statistically enriched on certain biological pathway network. Current gene pathway enrichment methods commonly consider local importance of genes in pathways without considering the interactions between genes. In this paper, we propose a gene pathway enrichment method (GIGSEA) based on improved TF-IDF algorithm. This method employs gene interaction data to calculate the influence of genes based on the local importance in a pathway as well as the global specificity. Computational experiment result shows that, compared with traditional gene set enrichment analysis method, our proposed method in this paper can find more specific enriched pathways related to phenotype with higher efficiency.