Antagonism between Gdf6a and retinoic acid pathways controls timing of retinal neurogenesis and growth of the eye in zebrafish.

Maintaining neurogenesis in growing tissues requires a tight balance between progenitor cell proliferation and differentiation. In the zebrafish retina, neuronal differentiation proceeds in two stages with embryonic retinal progenitor cells (RPCs) of the central retina accounting for the first rounds of differentiation, and stem cells from the ciliary marginal zone (CMZ) being responsible for late neurogenesis and growth of the eye. In this study, we analyse two mutants with small eyes that display defects during both early and late phases of retinal neurogenesis. These mutants carry lesions in gdf6a, a gene encoding a BMP family member previously implicated in dorsoventral patterning of the eye. We show that gdf6a mutant eyes exhibit expanded retinoic acid (RA) signalling and demonstrate that exogenous activation of this pathway in wild-type eyes inhibits retinal growth, generating small eyes with a reduced CMZ and fewer proliferating progenitors, similar to gdf6a mutants. We provide evidence that RA regulates the timing of RPC differentiation by promoting cell cycle exit. Furthermore, reducing RA signalling in gdf6a mutants re-establishes appropriate timing of embryonic retinal neurogenesis and restores putative stem and progenitor cell populations in the CMZ. Together, our results support a model in which dorsally expressed gdf6a limits RA pathway activity to control the transition from proliferation to differentiation in the growing eye

Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations

The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydneys suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure

Intelligent path planning for civil infrastructure inspection with multi-rotor aerial vehicles

AbstractThis paper presents the development of algorithms for high-level control and intelligent path planning of multi-rotor aerial vehicles (MAVs) in the tasks of inspecting civil infrastructure. After revisiting the multicopter modeling, we describe the hierarchy of high-level control for MAVs and develop optimization algorithms for generating optimal paths and enabling automatic flight during inspection tasks, making use of the digital twin technology. A co-simulation framework is then established to simulate and evaluate inspection mission scenarios, integrating these essential components. Real-world examples from built infrastructure illustrate this concept. An advantage of this approach is its ability to rigorously test, validate, verify, and evaluate MAV operations under abnormal conditions without requiring physical implementation or field tests. This significantly reduces testing efforts throughout the development cycle, ensuring optimal cooperation, safety, smoothness, fault tolerance, and energy efficiency. The methodology is validated through simulations and real-world inspection of a monorail bridge.</jats:p

Development of Measure Yourself Concerns and Wellbeing for informal caregivers of people with cancer – a multicentred study

Purpose: Measure Yourself Concerns and Wellbeing (MYCaW) is a validated person-centred measure of the concerns and wellbeing of people affected by cancer. Research suggests that the concerns of informal caregivers (ICs) are as complex and severely rated as people with cancer, yet MYCaW has only been used to represent cancer patients’ concerns and wellbeing. This paper reports on the development of a new qualitative coding framework for MYCaW to capture the concerns of ICs, to better understand the needs of this group. Methods: This multicentred study involved collection of data from ICs receiving support from two UK cancer support charities (Penny Brohn UK and Cavendish Cancer Care). Qualitative codes were developed through a detailed thematic analysis of ICs’ stated concerns. Results: Thematic analysis of IC questionnaire data identified key themes which were translated into a coding framework with two overarching sections; 1. ‘informal caregiver concerns for self’ and 2. ‘informal caregiver concerns for the person with cancer’. Supercategories with specific accompanying codes were developed for each section. Two further rounds of framework testing across different cohorts allowed for iterative development and refinement of the framework content. Conclusions: This is the first person-centred tool specifically designed for capturing IC’s concerns through their own words. This coding framework will allow for IC data to be analysed using a rigorous and reproducible method, and therefore reported in a standardised way. This may also be of interest to those exploring the needs of ICs of people in other situations

Stag hunt game-based approach for cooperative UAVs

Unmanned aerial vehicles (UAVs) are being employed in many areas such as photography, emergency, entertainment, defence, agriculture, forestry, mining and construction. Over the last decade, UAV technology hasfound applicationsin numerous construction project phases, ranging from site mapping, progress monitoring, building inspection, damage assessments, and material delivery. While extensive studies have been conducted on the advantages of UAVs for various construction-related processes, studies on UAV collaboration to improve the task capacity and efficiency are still scarce. This paper proposes a new cooperative path planning algorithm for multiple UAVs based on the stag hunt game and particle swarm optimization (PSO). First, a cost function for each UAV is defined, incorporating multiple objectives and constraints. The UAV game framework is then developed to formulate the multi-UAV path planning into the problem of finding payoff-dominant e quilibrium. Next, a PSO-based algorithm is proposed to obtain optimal paths for the UAVs. Simulation results for a large construction site inspected by three UAVs indicate the effectiveness of the proposed algorithm in generating feasible and efficient flight paths for UAV formation during the inspection task

Deep-learning based visualization tool for air pollution forecast

This paper presents a comprehensive visualization tool that integrates real-time observation and sensing data with various forecasting models, including both numerical and deep-learning approaches. The developed software framework efficiently manages data flow, configures forecasting models, and visualizes monitoring and prediction information. Additionally, the tool features a newly-developed WebApp dashboard, providing users with an interactive platform for real-time data access and decision-making. This web-based deep-learning framework is designed to enhance environmental monitoring and forecasting, providing a valuable tool for both the authority as well as the general public

Current and prospective pharmacological targets in relation to antimigraine action

Migraine is a recurrent incapacitating neurovascular disorder characterized by unilateral and throbbing headaches associated with photophobia, phonophobia, nausea, and vomiting. Current specific drugs used in the acute treatment of migraine interact with vascular receptors, a fact that has raised concerns about their cardiovascular safety. In the past, α-adrenoceptor agonists (ergotamine, dihydroergotamine, isometheptene) were used. The last two decades have witnessed the advent of 5-HT1B/1D receptor agonists (sumatriptan and second-generation triptans), which have a well-established efficacy in the acute treatment of migraine. Moreover, current prophylactic treatments of migraine include 5-HT2 receptor antagonists, Ca2+ channel blockers, and β-adrenoceptor antagonists. Despite the progress in migraine research and in view of its complex etiology, this disease still remains underdiagnosed, and available therapies are underused. In this review, we have discussed pharmacological targets in migraine, with special emphasis on compounds acting on 5-HT (5-HT1-7), adrenergic (α1, α2, and β), calcitonin gene-related peptide (CGRP 1 and CGRP2), adenosine (A1, A2, and A3), glutamate (NMDA, AMPA, kainate, and metabotropic), dopamine, endothelin, and female hormone (estrogen and progesterone) receptors. In addition, we have considered some other targets, including gamma-aminobutyric acid, angiotensin, bradykinin, histamine, and ionotropic receptors, in relation to antimigraine therapy. Finally, the cardiovascular safety of current and prospective antimigraine therapies is touched upon

Dempster-Shafer ensemble learning framework for air pollution nowcasting

Deep-learning has emerged as a powerful approach to significantly improve forecast accuracy for air quality estimation. Several models have been developed, demonstrating their own merits in some scenarios and for certain pollutants. In nowcasting, the prediction of air pollution over a small time period essentially demands accurate and reliable estimates, especially in the event cases. From these, selecting the most suitable model to achieve the required forecast performance remains challenging. This paper presents an ensemble framework based on the Dempster-Shafer theory for data fusion to identify the most accurate and reliable forecasts of air pollution obtained from multiple deep neural network models. Our framework is evaluated against three popular machine learning methods, namely, LightGBM, Random Forest, and XGBoost. Experiments are conducted on two horizons: 6-hour and 12-hour predictions using real-world air quality data collected from state-run monitoring stations and low-cost wireless sensor networks.</jats:p