Journal: Journal of Parasitic Diseases: Official Organ of the Indian Society for Parasitology

Article Title: Spatio-temporal distribution analysis of zoonotic cutaneous leishmaniasis in Qom Province, Iran

doi: 10.1007/s12639-018-1036-5

Figure Lengend Snippet: Spatial autocorrelation–Moran’s I plotted on the study area map and overlaid with cutaneous leishmaniasis incidence in Qom province, central Iran during 2009–2017

Article Snippet: In order to investigate spatial variations of CL, the incidence of disease was calculated in all of 23 villages of the study years based on formula (Gordis 2009 ): Incidence = N u m b e r o f n e w c a s e s o c c u r i n g i n t h e p o p u l a t i o n i n a g i v e n p e r i o d o f t i m e N u m b e r o f p e o p l e e x p o s e d t o t h e r i s k o f t h e d i s e a s e i n t h e s a m e p e r i o d × 10 N Afterward, spatio-temporal analysis of the disease was performed using two analyzes: Kriging method and Spatial Moron correlation in GIS environment. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Fig. 1 caption a7 Incidence of cutaneous leishmaniasis incidence in the Qom province, central Iran, from 2009 to 2017 Kriging method is one of interpolation methods (Kleijnen 2009 ).

Techniques:

Journal: Diabetologia

Article Title: Association of CREBRF variants with obesity and diabetes in Pacific Islanders from Guam and Saipan

doi: 10.1007/s00125-019-4932-z

Figure Lengend Snippet: (a) ‘Bean’ plot of the distribution of BMI according to genotypes at rs12513649. Grey horizonal lines represent individual values, with the length of the line corresponding to the number of observations at each level. The symmetrical plots represent the density at each BMI level, estimated using the kernel density estimation function in SAS. Black horizontal bars represent the mean value. β=1.55 kg/m2 per copy of the G allele; p=0.0026. (b) ‘Bean’ plot of the distribution of BMI according to genotypes at rs373863828. β=1.48 kg/m2 per copy of the A allele; p=0.033. (c) Prevalence of diabetes according to genotype at rs12513649. OR 0.63 per copy of the G allele; p=0.0063. (d) Prevalence of diabetes according to genotype at rs373863828. OR 0.49 per copy of the A allele; p=0.0022. (e) Meta-analysis of the association of the A allele at rs373863828 with BMI, including data from the present study (labeled ‘Guam/Saipan’). Data are presented as the regression coefficient (β, kg/m2 per copy of the A allele) with 95% CI. (f) Meta-analysis of the association of the A allele at rs373863828 with diabetes. Data are presented as OR per copy of the A allele with 95% CI. NZ, New Zealand

Article Snippet: The symmetrical plots represent the density at each BMI level, estimated using the kernel density estimation function in SAS.

Techniques: Labeling

Journal: Scientific Reports

Article Title: A prediction model for soil heavy metal content based on improved tensor completion

doi: 10.1038/s41598-025-07565-7

Figure Lengend Snippet: The workflow of the improved tensor completion algorithm under the C2F framework. The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

Article Snippet: The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: A prediction model for soil heavy metal content based on improved tensor completion

doi: 10.1038/s41598-025-07565-7

Figure Lengend Snippet: Kriging interpolation results for different metals. The interpolation results in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

Article Snippet: The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

Techniques: Generated, Software

Journal: Scientific Reports

Article Title: A prediction model for soil heavy metal content based on improved tensor completion

doi: 10.1038/s41598-025-07565-7

Figure Lengend Snippet: As completion process. The original heatmap in Fig. 3 are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

Article Snippet: The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

Techniques: Generated, Software