Reproducibility and effect of tissue composition on cerebellar GABA MRS in an elderly population.

Magnetic resonance spectroscopy (MRS) provides a valuable tool to non-invasively detect brain gamma-amino butyric acid (GABA) in vivo. GABAergic dysfunction has been observed in the aging cerebellum. Studying cerebellar GABA changes is of considerable interest in understanding certain age-related motor disorders. However, little is known about the reproducibility of GABA MRS in an aged population. Therefore, this study aimed to explore the feasibility and reproducibility of GABA MRS in the aged cerebellum at 3.0 Tesla and to examine the effect of differing tissue composition on GABA measurements. MRI and 1H MRS exams were performed on 10 healthy elderly volunteers (mean age 75.2 years ± 6.5 years) using a 3.0 Tesla Siemens Tim Trio scanner. Among them, 5 subjects were scanned twice to assess short-term reproducibility. The MEGA-PRESS J-editing sequence was used for GABA detection in two volumes of interest (VOIs) in left and right cerebellar dentate. MRS data processing and quantification were performed with LCModel 6.3-0L using two separate basis sets, generated from density matrix simulations using published values for chemical shifts an

Thalamic GABA levels and Occupational Manganese Neurotoxicity: Association with Exposure Levels and Brain MRI

Excessive occupational exposure to Manganese (Mn) has been associated with clinical symptoms resembling idiopathic Parkinson’s disease (IPD), impairing cognitive and motor functions. Several studies point towards an involvement of the brain neurotransmitter system in Mn intoxication, which is hypothesized to be disturbed prior to onset of symptoms. Edited Magnetic Resonance Spectroscopy (MRS) offers the unique possibility to measure γ-amminobutyric acid (GABA) and other neurometabolites in vivo non-invasively in workers exposed to Mn. In addition, the property of Mn as Magnetic Resonance Imaging (MRI) contrast agent may be used to study Mn deposition in the human brain. In this study, using MRI, MRS, personal air sampling at the working place, work history questionnaires, and neurological assessment (UPDRS-III), the effects of chronic Mn exposure on the thalamic GABAergic system was studied in a group of welders (N = 39) with exposure to Mn fumes in a typical occupational setting. Two subgroups of welders with different exposure levels (Low: N = 26; mean air Mn = 0.13 ± 0.1 mg/m3; High: N = 13; mean air Mn = 0.23 ± 0.18 mg/m3), as well as unexposed control workers (N = 22, mean air Mn = 0.002 ± 0.001 mg/m3) were recruited. The group of welders with higher exposure showed a significant increase of thalamic GABA levels by 45% (p < 0.01, F(1,33) = 9.55), as well as significantly worse performance in general motor function (p < 0.01, F(1,33) = 11.35). However, welders with lower exposure did not differ from the controls in GABA levels or motor performance. Further, in welders the thalamic GABA levels were best predicted by past-12-months exposure levels and were influenced by the Mn deposition in the substantia nigra and globus pallidus. Importantly, both thalamic GABA levels and motor function displayed a non-linear pattern of response to Mn exposure, suggesting a threshold effect

Influences of Ultrafine Ti(C, N) on the Sintering Process and Mechanical Properties of Micron Ti(C, N)-Based Cermets

For investigating the influence mechanism underlying ultrafine Ti(C, N) within micron Ti(C, N)-based cermets, three cermets including diverse ultrafine Ti(C, N) contents were employed. In addition, for the prepared cermets, their sintering process, microstructure, and mechanical properties were systematically studied. According to our findings, adding ultrafine Ti(C, N) primarily affects the densification and shrinkage behavior in the solid-state sintering stage. Additionally, material-phase and microstructure evolution were investigated under the solid-state stage from 800 to 1300 °C. Adding ultrafine Ti(C, N) enhanced the diffusion and dissolution behavior of the secondary carbide (Mo2C, WC, and (Ta, Nb)C) under a lower sintering temperature of 1200 °C. Further, as sintering temperature increased, adding ultrafine Ti(C, N) enhanced heavy element transformation behaviors in the binder phase and accelerated solid-solution (Ti, Me) (C, N) phase formation. When the addition of ultrafine Ti(C, N) reached 40 wt%, the binder phase had increased its liquefying speed. Moreover, the cermet containing 40 wt% ultrafine Ti(C, N) displayed superb mechanical performances

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

Protocol for a gallbladder cancer registry study in China: the Chinese Research Group of Gallbladder Cancer (CRGGC) study

Introduction Gallbladder cancer (GBC), the sixth most common gastrointestinal tract cancer, poses a significant disease burden in China. However, no national representative data are available on the clinical characteristics, treatment and prognosis of GBC in the Chinese population.Methods and analysis The Chinese Research Group of Gallbladder Cancer (CRGGC) study is a multicentre retrospective registry cohort study. Clinically diagnosed patient with GBC will be identified from 1 January 2008 to December, 2019, by reviewing the electronic medical records from 76 tertiary and secondary hospitals across 28 provinces in China. Patients with pathological and radiological diagnoses of malignancy, including cancer in situ, from the gallbladder and cystic duct are eligible, according to the National Comprehensive Cancer Network 2019 guidelines. Patients will be excluded if GBC is the secondary diagnosis in the discharge summary. The demographic characteristics, medical history, physical examination results, surgery information, pathological data, laboratory examination results and radiology reports will be collected in a standardised case report form. By May 2021, approximately 6000 patient with GBC will be included. The clinical follow-up data will be updated until 5 years after the last admission for GBC of each patient. The study aimed (1) to depict the clinical characteristics, including demographics, pathology, treatment and prognosis of patient with GBC in China; (2) to evaluate the adherence to clinical guidelines of GBC and (3) to improve clinical practice for diagnosing and treating GBC and provide references for policy-makers.Ethics and dissemination The protocol of the CRGGC has been approved by the Committee for Ethics of Xinhua Hospital, Shanghai Jiao Tong University School of Medicine (SHEC-C-2019–085). All results of this study will be published in peer-reviewed journals and presented at relevant conferences.Trial registration number NCT04140552, Pre-results