In Vivo Measurement of Brain GABA Concentrations by Magnetic Resonance Spectroscopy in Smelters Occupationally Exposed to Manganese

Background: Exposure to excessive manganese (Mn) levels is known to induce psychiatric and motor disorders including parkinsonian symptoms. Therefore finding a reliable means for early detection of Mn neurotoxicity is desirable. Objectives: Our goal was to study whether in-vivo brain levels of gamma-aminobutyric acid (GABA), N-acetylaspartate (NAA) and other brain metabolites in smelters were altered as a consequence of Mn exposure. Methods: T1-weighted MRI was used to visualize Mn deposition in the brain. Magnetic resonance spectroscopy (MRS) was used to quantify concentrations of NAA, glutamate and other brain metabolites in globus pallidus, putamen, thalamus, and frontal cortex from a well-established cohort of 10 male Mn-exposed smelters and 10 male age-matched control subjects. The MEGA-PRESS MRS sequence was used to determine GABA levels in a region encompassing the thalamus and adjacent parts of the basal ganglia ("GABA-VOI"). Results: Seven out of ten exposed subjects showed clear T1-hyperintense signals in the globus pallidus indicating Mn accumulation. We found a significant increase (82%; p=0.014) of GABA/tCr in the GABA-VOI of Mn-exposed subjects, as well as a distinct decrease (9%, p=0.04) of NAA/tCr in frontal cortex that strongly correlated (R= - 0.93, p<0.001) with cumulative Mn exposure. Conclusions: We demonstrated elevated GABA levels in the thalamus and adjacent basal ganglia and decreased frontal cortex NAA levels, indicating neuronal dysfunction in a brain area not primarily targeted by Mn. Therefore, the non-invasive in vivo MRS measurement of GABA and NAA may prove to be a powerful tool for detecting presymptomatic effects of Mn neurotoxicity

Optimal Operation of a Single Unit with an Adjustable Blade in an Interbasin Water Transfer Pumping Station Based on Successive Approximation Discretization for Blade Angle

In the mathematical model of the optimal operation of a single pump unit with a fully adjustable blade in the Chinese South-to-North Water Diversion Project, the decision variable, namely, blade angle, was uniformly dispersed in its feasible region in a fixed step size in consideration of the requirements of the pumping head and matching motor power. 1D dynamic programming was applied to solve the original model. When the obtained blade for each time period was set as the middle reference value and the discrete region of the blade was reduced to two times of the step size in the previous time, the blade angle was correspondingly reduced and dispersed in this new discrete region, thus eliminating unnecessary optimization space. Then, 1D dynamic programming was applied again to optimize the blade angle of the single pump unit further. After a series of successive approximation discretization of the blade angle and corresponding solutions of the obtained mathematical model, the optimization process was considered completed when the given control precision met the requirement. A case study showed that under typical operating conditions, the total cost saving percentage of water pumping quantity reached 0.048%–0.463%, with an average saving rate of 0.192%. The actual total water pumping quantity of the single unit decreased by 2153 m3 on the average. The proposed discretization method exerted a better optimization effect and needed a smaller computational amount compared with traditional one-time uniform discretization in the original feasible region of the blade angle

A topological framework for real-time 3D weather radar data processing

Real-time 3D weather radar data processing makes it possible to efficiently simulate meteorological processes in digital Earth and support the assessment of meteorological disasters. The current real-time meteorological operation system can only deal with radar data within 2D space as a flat map and lacks supporting 3D characteristics. Thus, valuable 3D information imbedded in radar data cannot be completely presented to meteorological experts. Due to the large amount of data and high complexity of radar data 3D operation, regular methods are not competent for supporting real-time 3D radar data processing and representation. This study aims to perform radar data 3D operations with high efficiency and instant speed to provide real-time 3D support for the meteorological field. In this paper, a topological framework composed of basic inner topological objects is proposed along with the quadtree structure and LOD architecture, based on which 3D operations on radar data can be conducted in a split second and 3D information can be presented in real time. As the applications of the proposed topological framework, two widely used 3D algorithms in the meteorological field are also implemented in this paper. Finally, a case study verifies the applicability and validity of the proposed topological framework

Activity-based process construction for participatory geo-analysis

Due to its advantages in participation and collaboration, participatory geo-analysis has been used for solving different types of geographical issues. Participatory geo-analysis is usually a complicated process consisting of various tasks that may involve different multidisciplinary participants. Previous studies have focused primarily on how to improve participation in specific individual tasks, especially idea discussion and decision-making, but they have ignored collaboration throughout the entire process. During a complete participatory geo-analysis effort, the various participants should concentrate on their familiar work and fully exploit their talents to perform work collaboratively. Therefore, we propose an activity-based process construction method to assist different participants in understanding the geo-analysis process and in concentrating on their familiar work. Eight core activities are established for the geo-analysis process: (1) context definition and resource collection, (2) data processing, (3) data analysis, (4) data visualization, (5) geo-analysis model construction, (6) model effectiveness evaluation, (7) geographical simulation, and (8) decision making. By using a visualization-based method, different activities can be linked together to represent the entire analytical process. Moreover, each activity is designed via a specialized web-based workspace in which online tools and resources are accessed to assist the participants with their geo-analysis practices. A prototype system was developed based on the proposed method, and a case study on a participatory risk assessment of coronavirus disease 2019 (COVID-19) was demonstrated using this system. The result suggests that the proposed method can promote collaboration among participants with different backgrounds, and verifies its feasibility and suitability

Customizable process design for collaborative geographic analysis

Collaborative geographic analysis can lead to better outcomes but requires complicated interactions among participants, support resources and analytic tools. A process expression with explicit structure and content can help coordinate and guide these interactions. For different geographic problems, the structure and content of collaborative geographic analysis are generally distinct. Since the process structure embodies the pathway of problem-solving and the process content contains the information flow and internal interactions, both the structure and the content of the process expression must be clarified during process customization. However, relevant studies concerning the collaborative geographic analysis process mainly focus on the process structure, which remains a “black box” in terms of the process content, especially the internal interactions. Therefore, this article designs a customizable process expression model that takes both process structure and content into account and proposes a corresponding process customization method for collaborative geographic analysis. Additionally, a support method for geographic analysis process implementation is also provided. To verify the feasibility and capability, these methods were implemented in a prototype system, and a case study on traffic noise assessment was conducted. The results suggest that the proposed strategy can effectively improve geographic analysis by customizing processes, guiding participants, performing interactions, and recording operations throughout the process

Iterative integration of deep learning in hybrid Earth surface system modelling

Earth system modelling (ESM) is essential for understanding past, present and future Earth processes. Deep learning (DL), with the data-driven strength of neural networks, has promise for improving ESM by exploiting information from Big Data. Yet existing hybrid ESMs largely have deep neural networks incorporated only during the initial stage of model development. In this Perspective, we examine progress in hybrid ESM, focusing on the Earth surface system, and propose a framework that integrates neural networks into ESM throughout the modelling lifecycle. In this framework, DL computing systems and ESM-related knowledge repositories are set up in a homogeneous computational environment. DL can infer unknown or missing information, feeding it back into the knowledge repositories, while the ESM-related knowledge can constrain inference results of the DL. By fostering collaboration between ESM-related knowledge and DL systems, adaptive guidance plans can be generated through question-answering mechanisms and recommendation functions. As users interact iteratively, the hybrid system deepens its understanding of their preferences, resulting in increasingly customized, scalable and accurate guidance plans for modelling Earth processes. The advancement of this framework necessitates interdisciplinary collaboration, focusing on explainable DL and maintaining observational data to ensure the reliability of simulations