Surgical treatment and reproductive outcomes in caesarean scar pregnancy at a single center.

BACKGROUND: To investigate factors associated with different reproductive outcomes in patients with Caesarean scar pregnancies (CSPs). METHODS: Between May 2017 and July 2022, 549 patients underwent ultrasound-guided uterine aspiration and laparoscopic scar repair at the Gynaecology Department of Hubei Maternal and Child Health Hospital. Ultrasound-guided uterine aspiration was performed in patients with type I and II CSPs, and laparoscopic scar repair was performed in patients with type III CSP. The reproductive outcomes of 100 patients with fertility needs were followed up and compared between the groups. RESULTS: Of 100 patients, 43% had live births (43/100), 19% had abortions (19/100), 38% had secondary infertility (38/100), 15% had recurrent CSPs (RCSPs) (15/100). The reproductive outcomes of patients with CSPs after surgical treatment were not correlated with age, body mass index, time of gestation, yields, abortions, Caesarean sections, length of hospital stay, weeks of menopause during treatment, maximum diameter of the gestational sac, thickness of the remaining muscle layer of the uterine scar, type of CSP, surgical method, uterine artery embolisation during treatment, major bleeding, or presence of uterine adhesions after surgery. Abortion after treatment was the only risk factor affecting RCSPs (odds ratio 11.25, 95% confidence interval, 3.302-38.325; P < 0.01) and it had a certain predictive value for RCSP occurrence (area under the curve, 0.741). CONCLUSIONS: The recurrence probability of CSPs was low, and women with childbearing intentions after CSPs should be encouraged to become pregnant again. Abortion after CSP is a risk factor for RCSP. No significant difference in reproductive outcomes was observed between the patients who underwent ultrasound-guided uterine aspiration and those who underwent laparoscopic scar repair for CSP.

TransCrack: revisiting fine-grained road crack detection with a transformer design.

Prior convolution-based road crack detectors typically learn more abstract visual representation with increasing receptive field via an encoder-decoder architecture. Despite the promising accuracy, progressive spatial resolution reduction causes semantic feature blurring, leading to coarse and incontiguous distress detection. To these ends, an alternative sequence-to-sequence perspective with a transformer network termed TransCrack is introduced for road crack detection. Specifically, an image is decomposed into a grid of fixed-size crack patches, which is flattened with position embedding into a sequence. We further propose a pure transformer-based encoder with multi-head reduced self-attention modules and feed-forward networks for explicitly modelling long-range dependencies from the sequential input in a global receptive field. More importantly, a simple decoder with cross-layer aggregation architecture is developed to incorporate global with local attentions across different regions for detailed feature recovery and pixel-wise crack mask prediction. Empirical studies are conducted on three publicly available damage detection benchmarks. The proposed TransCrack achieves a state-of-the-art performance over all counterparts by a substantialmargin, and qualitative results further demonstrate its superiority in contiguous crack recognition and fine-grained profile extraction. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

3D-DFM: Anchor-Free Multimodal 3-D Object Detection With Dynamic Fusion Module for Autonomous Driving.

Recent advances in cross-modal 3D object detection rely heavily on anchor-based methods, and however, intractable anchor parameter tuning and computationally expensive postprocessing severely impede an embedded system application, such as autonomous driving. In this work, we develop an anchor-free architecture for efficient camera-light detection and ranging (LiDAR) 3D object detection. To highlight the effect of foreground information from different modalities, we propose a dynamic fusion module (DFM) to adaptively interact images with point features via learnable filters. In addition, the 3D distance intersection-over-union (3D-DIoU) loss is explicitly formulated as a supervision signal for 3D-oriented box regression and optimization. We integrate these components into an end-to-end multimodal 3D detector termed 3D-DFM. Comprehensive experimental results on the widely used KITTI dataset demonstrate the superiority and universality of 3D-DFM architecture, with competitive detection accuracy and real-time inference speed. To the best of our knowledge, this is the first work that incorporates an anchor-free pipeline with multimodal 3D object detection.

From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks.

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.