MAD-UNet: A Multi-Region UAV Remote Sensing Network for Rural Building Extraction

For the development of an idyllic rural landscape, an accurate survey of rural buildings is essential. The extraction of rural structures from unmanned aerial vehicle (UAV) remote sensing imagery is prone to errors such as misclassifications, omissions, and subpar edge detailing. This study introduces a multi-scale fusion and detail enhancement network for rural building extraction, termed the Multi-Attention-Detail U-shaped Network (MAD-UNet). Initially, an atrous convolutional pyramid pooling module is integrated between the encoder and decoder to enhance the main network’s ability to identify buildings of varying sizes, thereby reducing omissions. Additionally, a Multi-scale Feature Fusion Module (MFFM) is constructed within the decoder, utilizing superficial detail features to refine the layered detail information, which improves the extraction of small-sized structures and their edges. A coordination attention mechanism and deep supervision modules are simultaneously incorporated to minimize misclassifications. MAD-UNet has been tested on a private UAV building dataset and the publicly available Wuhan University (WHU) Building Dataset and benchmarked against models such as U-Net, PSPNet, DeepLabV3+, HRNet, ISANet, and AGSCNet, achieving Intersection over Union (IoU) scores of 77.43% and 91.02%, respectively. The results demonstrate its effectiveness in extracting rural buildings from UAV remote sensing images across different regions

Enhancing Air Quality Forecasting: A Novel Spatio-Temporal Model Integrating Graph Convolution and Multi-Head Attention Mechanism

Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for predicting air quality spatio-temporal variations is introduced. The model, named graph long short-term memory with multi-head attention (GLSTMMA), is designed to capture the temporal patterns and spatial relationships within multivariate time series data related to air quality. The GLSTMMA model utilizes a hybrid neural network architecture to effectively learn the complex dependencies and correlations present in the data. The extraction of spatial features related to air quality involves the utilization of a graph convolutional network (GCN) to collect air quality data based on the geographical distribution of monitoring sites. The resulting graph structure is imported into a long short-term memory (LSTM) network to establish a Graph LSTM unit, facilitating the extraction of temporal dependencies in air quality. Leveraging a Graph LSTM unit, an encoder-multiple-attention decoder framework is formulated to enable a more profound and efficient exploration of spatio-temporal correlation features within air quality time series data. The research utilizes the 2019–2021 multi-source air quality dataset of Qinghai Province for experimental assessment. The results indicate that the model effectively leverages the impact of multi-source data, resulting in optimal accuracy in predicting six air pollutants

Efficacy and safety of atogepant, a small molecule CGRP receptor antagonist, for the preventive treatment of migraine: a systematic review and meta-analysis

Abstract Background Migraine is one of the most common diseases worldwide while current treatment options are not ideal. New therapeutic classes of migraine, the calcitonin gene-related peptide (CGRP) antagonists, have been developed and shown considerable effectiveness and safety. The present study aimed to systematically evaluate the efficacy and safety of atogepant, a CGRP antagonist, for migraine prophylaxis from the results of randomized controlled trials (RCTs). Methods The Cochrane Library, Embase, PubMed and https://www.clinicaltrials.gov/ were searched for RCTs that compared atogepant with placebo for migraine prophylaxis from inception of the databases to Feb 1, 2024. Outcome data involving efficacy and safety were combined and analyzed using Review Manager Software version 5.3 (RevMan 5.3). For each outcome, risk ratios (RRs) or standardized mean difference (SMD) were calculated. Results 4 RCTs with a total of 2813 subjects met our inclusion criteria. The overall effect estimate showed that atogepant was significantly superior to placebo in terms of the reduction of monthly migraine (SMD − 0.40, 95% CI -0.46 to -0.34) or headache (SMD − 0.39, 95% CI -0.46 to -0.33) days, the reduction of acute medication use days (SMD − 0.45, 95% CI -0.51 to -0.39) and 50% responder rate (RR 1.66, 95% CI 1.46 to 1.89), while no dose-related improvements were found between different dosage groups. For the safety, significant number of patients experienced treatment-emergent adverse events (TEAEs) with atogepant than with placebo (RR 1.10, 95% CI 1.02–1.21) while there was no obvious difference between the five dosage groups. Most TEAEs involved constipation (RR 2.55, 95% CI 1.91–3.41), nausea (RR 2.19, 95% CI 1.67–2.87) and urinary tract infection (RR 1.49, 95% CI 1.05–2.11). In addition, a high dosage of atogepant may also increase the risk of treatment-related TEAEs (RR 1.64, 95% CI 1.02–2.63) and fatigue (RR 3.07, 95% CI 1.13–8.35). Conclusions This meta-analysis suggests that atogepant is effective and tolerable for migraine prophylaxis including episodic or chronic migraine compared with placebo. It is critical to weigh the benefits of different doses against the risk of adverse events in clinical application of atogepant. Longer and multi-dose trials with larger sample sizes are required to verify the current findings