Phlebotomine sand fly survey in the focus of leishmaniasis in Madrid, Spain (2012-2014): seasonal dynamics, Leishmania infantum infection rates and blood meal preferences

BACKGROUND: An unusual increase of human leishmaniasis cases due to Leishmania infantum is occurring in an urban area of southwestern Madrid, Spain, since 2010. Entomological surveys have shown that Phlebotomus perniciosus is the only potential vector. Direct xenodiagnosis in hares (Lepus granatensis) and rabbits (Oryctolagus cuniculus) collected in the focus area proved that they can transmit parasites to colonized P. perniciosus. Isolates were characterized as L. infantum. The aim of the present work was to conduct a comprehensive study of sand flies in the outbreak area, with special emphasis on P. perniciosus. METHODS: Entomological surveys were done from June to October 2012-2014 in 4 stations located close to the affected area. Twenty sticky traps (ST) and two CDC light traps (LT) were monthly placed during two consecutive days in every station. LT were replaced every morning. Sand fly infection rates were determined by dissecting females collected with LT. Molecular procedures applied to study blood meal preferences and to detect L. infantum were performed for a better understanding of the epidemiology of the outbreak. RESULTS: A total of 45,127 specimens belonging to 4 sand fly species were collected: P. perniciosus (75.34%), Sergentomyia minuta (24.65%), Phlebotomus sergenti (0.005%) and Phlebotomus papatasi (0.005%). No Phlebotomus ariasi were captured. From 3203 P. perniciosus female dissected, 117 were infected with flagellates (3.7%). Furthermore, 13.31% and 7.78% of blood-fed and unfed female sand flies, respectively, were found infected with L. infantum by PCR. The highest rates of infected P. perniciosus were detected at the end of the transmission periods. Regarding to blood meal preferences, hares and rabbits were preferred, although human, cat and dog blood were also found. CONCLUSIONS: This entomological study highlights the exceptional nature of the Leishmania outbreak occurring in southwestern Madrid, Spain. It is confirmed that P. perniciosus is the only vector in the affected area, with high densities and infection rates. Rabbits and hares were the main blood meal sources of this species. These results reinforce the need for an extensive and permanent surveillance in this region, and others of similar characteristics, in order to control the vector and regulate the populations of wild reservoirs.This study was partially sponsored and funded by: Dirección General de Salud Pública, Consejería de Sanidad, Comunidad de Madrid; Colegio de Veterinarios de Madrid; Colegio de Biólogos de Madrid and EU grant FP7-261504 EDENext (http://www.edenext.eu).S

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Common Limitations of Image Processing Metrics:A Picture Story

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.Comment: This is a dynamic paper on limitations of commonly used metrics. The current version discusses metrics for image-level classification, semantic segmentation, object detection and instance segmentation. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorshi

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Modeling the ecological niche: A Case Study on Bioclimatic Factors Related to the Distribution of Phlebotomus tobbi Adler & Theodor (Diptera: Psychodidae) in two endemic foci of Adana

PubMedID: 30753727Cutaneous leishmaniasis (CL) is a zoonotic infectious disease caused mainly by Leishmania infantum Nicolle, 1908 (Kinetoplastida:Trypanosomatida) transmitted by dominant species Phlebotomus tobbi Adler &Theodor (Diptera: Psychodidae) in Adana,Turkey. CL has been reported to be detected commonly in low-socioeconomic status population scattered in rural areas. The environmental determinants are relatively poorly understood, especially in Adana despite the fact that Adana is endemic foci of CL.The subject of this study was the current and future probability model of P. tobbi in the study areas, and to determine the underlying factors affecting its distribution. Sticky papers and CDC light traps were used for capturing the sand fly specimens.The current and future presence of P. tobbi was modeled using maximum entropy (MaxEnt) techniques. The predictive model indicated the presence of P. tobbi in the southeast and south part of the selected study area with 0.816 area under the curve (AUC) value.The model also implied that its survival could tend to expand with suitable climatic conditions in future (2070) with 0.798 AUC value. In addition, aspect, digital elevation model, BIO3, BIO 10, BIO12, and BIO14 were determined as the most influential variables for current and projected future. ArcGIS and MaxEnt software were used for the ecological niche model analysis to explore the ecological conditions of the disease. I suggest that produced models contribute to better understanding of epidemiology and controlling of vector-borne diseases. © The Author(s) 2019. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved

Investigation of the spatial distribution of sandfly species and cutaneous leishmaniasis risk factors by using geographical information system technologies in Karaisali district of Adana province, Turkey

PubMedID: 29097638Background & objectives: Cutaneous leishmaniasis displays two epidemiological routes of transmission, zoonotic cutaneous leishmaniasis (ZCL) which includes animal reservoir hosts in the transmission cycle and anthroponotic cutaneous leishmaniasis (ACL), where human is the sole source of infection for the vector sandflies. About 10–13% of CL cases are reported each year from Adana province in Turkey. The aim of this study was to develop a predictive model for determining the spatial risk level of cutaneous leishmaniasis in the Adana province, southern part of Turkey, in relation to environmental factors. Methods: Entomological survey was carried out between June 2015 and September 2016. Sandflies were collected from Karaisali district of the Adana province using light-traps and sticky papers. Sandfly fauna results were compared with environmental data obtained from field-survey, and examined with univariate and binary logistic regression in PASW statistical software. The ArcMap application of ArcGIS10.0. software was used for geographical adjustments to create maps and establish a risk model. Results: In total five sandfly species were identified in the study area, and three of them (Phlebotomus tobbi, P. neglectus/syriacus and P. perfiliewi) were detected as potential vectors of cutaneous leishmaniasis. The results showed that enhanced vegetation index (EVI) and emissivity band 31 (EMIS31) values are related to the distribution of these three species. Interpretation & conclusion: The created risk maps may provide useful information to guide the control programme interventions and prevent the economic loses in the future insecticide applications. They could be used to better understand the distribution of vectors, and determine the epidemiology and risk level of the CL. © 2017, Malaria Research Center. All rights reserved.Firat University Scientific Research Projects Management Unit: TSA-2015-3753The authors thank the Scientific Research Projects Coordination Unit of Cukurova University, Adana, Turkey, which supported the study (Project ID: TSA-2015-3753)

Geographical information systems in determination of cutaneous leishmaniasis spatial risk level based on distribution of vector species in Imamoglu Province, Adana

PubMedID: 28505264The Imamoglu district located in the southeast of Adana province in Turkey is an endemic focus of cutaneous leishmaniasis (CL) owing to dominancy of Phlebotomus tobbi, which is a probable vector of Leishmania infantum. About 11.26% of CL cases reported each year are from Imamoglu, Adana, and between 2008 and 2015, 223 cases of CL were reported. Leishmania infantum, which may be transmitted by P. tobbi, Phlebotomus neglectus/syriacus, and Phlebotomus perfiliewi, is referred as leishmaniasis factor in Adana. Thus, the aim of this study was to map the risk areas for each sand fly species using remote sensing images based on environmental factors and geographical characteristics. Two field works in two consecutive years (2013 and 2014) were conducted and six sand fly species were caught, four of which were identified as probable vector species. Field work results were compared with environmental data obtained from satellite images by univariate and binary logistic regression in PASW. ARCMAP 10.2 software was used for geographical adjustments, creating a database and estimating a risk model by using previous risk value formulas. The results showed that the distribution of three probable leishmaniasis vectors (P. tobbi, P. neglectus/syriacus, and P. perfiliewi) was associated with normalized difference vegetation index (NDVI), digital elevation model (DEM), night-time land surface temperature (LSTNIGHT), and emissivity (EMIS31) values, which were related to the local authorities, who take these findings into account when deciding on high risk areas for CL. © The Authors 2017.We thank Scientific Research Projects Coordination Unit of Cukurova University, which supported our study with a project ID of “IMYO2013BAP4.