Quantifying surface morphology of manufactured activated carbon and the waste coffee grounds using the Getis-Ord-Gi* statistic and Ripley’s K function

Activated carbon can be manufactured from waste coffee grounds via physical and/or chemical activation processes. However, challenges remain to quantify the differences in surface morphology between manufactured activated carbon granules and the waste coffee grounds. This paper presents a novel quantitative method to determine the quality of the physical and chemical activation processes performed in the presence of intensity inhomogeneity and identify surface characteristics of manufactured activated carbon granules and the waste coffee grounds. The spatial density was calculated by the Getis-Ord-Gi* statistic in scanning electron microscopy images. The spatial characteristics were determined by analyzing Ripley’s K function and complete spatial randomness. Results show that the method introduced in this paper is capable of distinguishing between manufactured activated carbon granules and the waste coffee grounds, in terms of surface morphology.

Quantifying surface morphology of manufactured activated carbon and the waste coffee grounds using the Getis-Ord-Gi* statistic and Ripley’s K function

Activated carbon can be manufactured from waste coffee grounds via physical and/or chemical activation processes. However, challenges remain to quantify the differences in surface morphology between manufactured activated carbon granules and the waste coffee grounds. This paper presents a novel quantitative method to determine the quality of the physical and chemical activation processes performed in the presence of intensity inhomogeneity and identify surface characteristics of manufactured activated carbon granules and the waste coffee grounds. The spatial density was calculated by the Getis-Ord-Gi* statistic in scanning electron microscopy images. The spatial characteristics were determined by analyzing Ripley’s K function and complete spatial randomness. Results show that the method introduced in this paper is capable of distinguishing between manufactured activated carbon granules and the waste coffee grounds, in terms of surface morphology.

85 Spatial heterogeneity of tumor associated macrophages in the tumor immune microenvironment in ccRCC

BackgroundTumor associated macrophages (TAM) stimulate tumor proliferation and facilitate immune escape via production of immunosuppressive cytokines. We hypothesize that non-random spatial clustering of TAMs within the tumor are associated with poor survival in ccRCC patients.MethodsTumor specimens were obtained from 41 patients with metastatic ccRCC who received immunotherapy (IT). Sections from the tumor core underwent multiplex immunofluorescence staining for CD68, CD163, and CD206. Digital pathologic analysis was used to convert the digital images to spatial point pattern plots (PPP). Ripley’s K function, the current standard metric for spatial heterogeneity, was utilized. Novel metrics were developed using a probability density function (PDF) for distances between cells, assuming that cells can be located anywhere with equal probability. Empirical histograms were generated from the PPPs. Deviation from the PDF demonstrates a non-random distribution. Deviations were quantified with the Kolmogorov-Smirnov (KS) test and Cramér-von Mises (CVM) criterion. Overall survival (OS) was assessed between groups stratified by the median value for each metric using Kaplan-Meier and log-rank analysis. Figure 1A.Results75 slides were analyzed from the 41 patients. The three metrics for measuring spatial heterogeneity had moderate and statistically significant correlation with each other (Spearman’s R: Ripley/KS=0.68, p<0.01; Ripley/CVM=0.54, p<0.01; KS/CVM=0.47, p<0.01; figure 1B). Using CVM, increasingly non-random distribution of the Tumor-CD68+ cell relationship was associated with worse OS (p<0.01, figure 1C), and increasingly non-random distribution of CD163+ cells suggested an association with worse OS without reaching statistical significance (p=0.06, figure 1C). No statistically significant associations were identified using the KS or Ripley’s K metrics.Abstract 85 Figure 1ConclusionsWe describe CVM and KS as novel metrics for measuring spatial heterogeneity of immune cells. Increased spatial heterogeneity of CD68+ TAMs and tumor cells was associated with worse OS in patients with metastatic ccRCC who received IT. These findings corroborate prior reports of TAMs eliciting an immunosuppressive effect on the tumor-immune microenvironment, and demonstrate the novel finding of a clinically significant effect of TAM spatial clustering on OS.

DEVELOPING APACHE SPARK BASED RIPLEY’S K FUNCTIONS FOR ACCELERATING SPATIOTEMPORAL POINT PATTERN ANALYSIS

Ripley’s K functions are powerful tools for studying the spatial arrangement or spatiotemporal distribution characteristics of geographic phenomena and events in spatial analysis and has been used in many fields. However, the K functions are compute-intensive for point-wise distance comparisons, edge correction and simulations for significance test. Although parallel computing technologies have been adopted to accelerate K functions, previous works haven’t extended the optimization from space to space-time dimension. This study presents an acceleration method for K functions upon state-of-the-art distributed computing framework Apache Spark, and four optimization strategies are leveraged to simplify calculation procedures and accelerate distributed computing respectively, including 1) spatiotemporal indexing based on R-tree with Sort-Tile-Recursive (STR) algorithm for reducing distance comparison when retrieving potential spatiotemporally neighbouring points; 2) Hash-Table-based caching for spatiotemporal edge correction weights reuse and reducing repetitive computation; 3) Spatiotemporal partitioning using KDB-tree as well as cylinder intersection redundancy strategy for decreasing ghost buffer redundancy in partitions and supporting near-balanced distributed processing; 4) Customized serialization of spatiotemporal objects and indexes for lowering the overhead of data transmission. Experiments verify the effectiveness and time efficiency of the proposed optimization strategies, and also evaluate the overall performance and scalability. Based on the proposed methods, a web-based visual analytics framework has been developed and publicly shared through GitHub, and four types of the distributed K functions are implemented, including space, space-time, local and cross K functions, which demonstrates its value on promoting geographical and socioeconomic studies.

EVALUATION AND QUANTIFICATION OF HEAT HOTSPOTS IN THE CHAPADA DOS GUIMARÃES-MT NATIONAL PARK

The study aimed to analyze the spatial distribution of heat sources inside and outside the Chapada dos Guimarães National Park (PNCG) in the State of Mato Grosso. The analyzes were performed through the estimate of kernel density (KDE) and Ripley's K function from 2005 to 2014. The data related to the number of hot spots were obtained from the National Institute for Space Research (INPE) from 2005 to 2014, and the vector files were acquired from the cartographic base of the Brazilian Institute of Geography and Statistics (IBGE). In the 10 years of analysis, 579 hot spots were detected in the PNCG, where it was found that the months of August and September had the highest incidence of hot spots in the park. The kernel maps demonstrated that most hotspots were observed in the years 2007, 2010 and 2012. The years 2005 and 2013 presented average densities and the years 2006, 2008, 2009, 2011 and 2014 indicated low density of the hot spots. Ripley's K function, calculated to observe the spatial distribution of the hot spots, rejected the hypothesis of complete spatial randomness (CSR), indicating that they showed a tendency to cluster during the study time series at the PNCG.