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Background: Growing evidence suggests important effects of body mass index (BMI) and metabolic status on neurodegenerative diseases. However, the roles of BMI and metabolic status on cognitive outcomes in Parkinson's disease (PD) may vary and are yet to be determined. Methods: In total, 139 PD patients from the whole PD cohort in Parkinson's Progression Markers Initiative database underwent complete laboratory measurements, demographic and anthropometric parameters at baseline, and were enrolled in this study. Further, they were categorized into 4 different BMI-metabolic status phenotypes using Adult Treatment Panel-III criteria. Motor and cognition scales at baseline and longitudinal changes after a 48-month follow-up were compared among the 4 groups. Repeated-measure linear mixed models were performed to compare PD-related biomarkers among BMI-metabolic status phenotypes across time. Results: We found that PD patients in the metabolically unhealthy normal weight group showed more cognitive decline in global cognition and visuospatial perception after a 48-month follow-up than those in the other 3 groups (p < 0.05). No difference was found in motor scales among different BMI-metabolic status phenotypes. Finally, compared to the metabolically healthy normal weight group, the metabolically healthy obesity group had lower CSF Aβ42 and serum neurofilament levels in repeated-measure linear mixed models adjusting for age, gender, APOE e4 carrier status, and years of education (p = 0.031 and 0.046, respectively). Conclusion: The MUNW phenotype was associated with a rapid cognitive decline in PD. [ABSTRACT FROM AUTHOR]

Background: Emerging evidence indicates that the apolipoprotein E (APOE) ε4 exacerbates α‐synuclein pathology. Objective: To determine whether APOE ε4 contributes to motor progression in early Parkinson's disease (PD). Methods: Longitudinal data were obtained from 384 patients with PD divided into APOE ε4 carriers (n = 85) and noncarriers (n = 299) in the Parkinson's Progression Marker Initiative. Participants underwent yearly motor assessments over a mean follow‐up period of 78.9 months. Repeated measures and linear mixed models were used to test the effects of APOE ε4. Results: The motor progression was significantly more rapid in patients with PD carrying APOE ε4 than in noncarriers (β = 0.283, P = 0.026, 95% confidence interval: 0.033–0.532). Through subgroup analysis, we found that the effect of APOE ε4 was significant only in patients with high amyloid β burden (β = 0.761, P < 0.001, 95% confidence interval: 0.0356–1.167). Conclusions: APOE ε4 may be associated with rapid motor progression in PD. © 2021 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

With the rapid growth of real-world graphs, the size of which can easily exceed the on-chip (board) storage capacity of an accelerator, processing large-scale graphs on a single Field Programmable Gate Array (FPGA) becomes difficult. The multi-FPGA acceleration is of great necessity and importance. Many cloud providers (e.g., Amazon, Microsoft, and Baidu) now expose FPGAs to users in their data centers, providing opportunities to accelerate large-scale graph processing. In this paper, we present a communication library, called FDGLib, which can easily scale out any existing single FPGA-based graph accelerator to a distributed version in a data center, with minimal hardware engineering efforts. FDGLib provides six APIs that can be easily used and integrated into any FPGA-based graph accelerator with only a few lines of code modifications. Considering the torus-based FPGA interconnection in data centers, FDGLib also improves communication efficiency using simple yet effective torus-friendly graph partition and placement schemes. We interface FDGLib into AccuGraph, a state-of-the-art graph accelerator. Our results on a 32-node Microsoft Catapult-like data center show that the distributed AccuGraph can be 2.32x and 4.77x faster than a state-of-the-art distributed FPGA-based graph accelerator ForeGraph and a distributed CPU-based graph system Gemini, with better scalability. [ABSTRACT FROM AUTHOR]

The variations among shapes, sizes, and locations of tumors are obstacles for accurate automatic segmentation. U-Net is a simplified approach for automatic segmentation. Generally, the convolutional or the dilated convolutional layers are used for brain tumor segmentation. However, existing segmentation methods of the significant dilation rates degrade the final accuracy. Moreover, tuning parameters and imbalance ratio between the different tumor classes are the issues for segmentation. The proposed model, known as Residual-Dilated Dense Atrous-Spatial Pyramid Pooling (RD2A) 3D U-Net, is found adequate to solve these issues. The RD2A is the combination of the residual connections, dilation, and dense ASPP to preserve more contextual information of small sizes of tumors at each level encoder path. The multi-scale contextual information minimizes the ambiguities among the tissues of the white matter (WM) and gray matter (GM) of the infant's brain MRI. The BRATS 2018, BRATS 2019, and iSeg-2019 datasets are used on different evaluation metrics to validate the RD2A. In the BRATS 2018 validation dataset, the proposed model achieves the average dice scores of 90.88, 84.46, and 78.18 for the whole tumor, the tumor core, and the enhancing tumor, respectively. We also evaluated on iSeg-2019 testing set, where the proposed approach achieves the average dice scores of 79.804, 77.925, and 80.569 for the cerebrospinal fluid (CSF), the gray matter (GM), and the white matter (WM), respectively. Furthermore, the presented work also obtains the mean dice scores of 90.35, 82.34, and 71.93 for the whole tumor, the tumor core, and the enhancing tumor, respectively on the BRATS 2019 validation dataset. Experimentally, it is found that the proposed approach is ideal for exploiting the full contextual information of the 3D brain MRI datasets. [ABSTRACT FROM AUTHOR]

The variations among shapes, sizes, and locations of tumors are obstacles for accurate automatic segmentation. U-Net is a simplified approach for automatic segmentation. Generally, the convolutional or the dilated convolutional layers are used for brain tumor segmentation. However, existing segmentation methods of the significant dilation rates degrade the final accuracy. Moreover, tuning parameters and imbalance ratio between the different tumor classes are the issues for segmentation. The proposed model, known as Residual-Dilated Dense Atrous-Spatial Pyramid Pooling (RD2A) 3D U-Net, is found adequate to solve these issues. The RD2A is the combination of the residual connections, dilation, and dense ASPP to preserve more contextual information of small sizes of tumors at each level encoder path. The multi-scale contextual information minimizes the ambiguities among the tissues of the white matter (WM) and gray matter (GM) of the infant's brain MRI. The BRATS 2018, BRATS 2019, and iSeg-2019 datasets are used on different evaluation metrics to validate the RD2A. In the BRATS 2018 validation dataset, the proposed model achieves the average dice scores of 90.88, 84.46, and 78.18 for the whole tumor, the tumor core, and the enhancing tumor, respectively. We also evaluated on iSeg-2019 testing set, where the proposed approach achieves the average dice scores of 79.804, 77.925, and 80.569 for the cerebrospinal fluid (CSF), the gray matter (GM), and the white matter (WM), respectively. Furthermore, the presented work also obtains the mean dice scores of 90.35, 82.34, and 71.93 for the whole tumor, the tumor core, and the enhancing tumor, respectively on the BRATS 2019 validation dataset. Experimentally, it is found that the proposed approach is ideal for exploiting the full contextual information of the 3D brain MRI datasets. [ABSTRACT FROM AUTHOR]

• Proposed a model for optimizing maintenance decisions with multi-objective failure priority ranking. • Employed the TOPSIS to prioritize the maintenance of failed buses based on indicators such as transport capacity loss, downtime and maintenance cost. • Case study indicated that the adoption of the model provides a significant reduction in operating costs. There is a specific correlation between bus operational failures and work intensity. The hysteresis effect identified the correlation between the number of repairs and the bus capacity of urban bus fleets. Therefore, administrators can determine whether to perform maintenance on a specific bus based on the severity of detected potential failures. At this stage, administrators should make a maintenance decision after considering maintenance costs, operational losses, and bus capacity. As a result, we propose a model for optimizing maintenance decisions with multi-objective failure priority ranking in this article. We employ the TOPSIS to prioritize the maintenance of failed buses based on indicators such as transport capacity loss, downtime, and maintenance cost. Finally, the study verifies the model's validity by utilizing real data from urban bus operation and maintenance data in China. The outcomes of the case study indicate that implementing the model can reduce operating expenses by 18.8143% when considering a corrective maintenance cost of 300. The model adoption provides a significant reduction in operating costs. [ABSTRACT FROM AUTHOR]