Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection.

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists' mosquito reports, while also shaping the spatial distribution of mosquito populations themselves.

Information fusion for infant age estimation from deciduous teeth using machine learning.

Objectives: Over the past few years, several methods have been proposed to improve the accuracy of age estimation in infants with a focus on dental development as a reliable marker. However, traditional approaches have limitations in efficiently combining information from different teeth and features. In order to address these challenges, this article presents a study on age estimation in infants with Machine Learning (ML) techniques, using deciduous teeth.

Evaluating artificial intelligence for comparative radiography.

Introduction: Comparative radiography is a forensic identification and shortlisting technique based on the comparison of skeletal structures in ante-mortem and post-mortem images. The images (e.g.

AIMSurv: First pan-European harmonized surveillance of invasive mosquito species of relevance for human vector-borne diseases.

Human and animal vector-borne diseases, particularly mosquito-borne diseases, are emerging or re-emerging worldwide. Six invasive mosquito (AIM) species were introduced to Europe since the 1970s: , , , , and . Here, we report the results of AIMSurv2020, the first pan-European surveillance effort for AIMs.

Computer-aided craniofacial superimposition validation study: the identification of the leaders and participants of the Polish-Lithuanian January Uprising (1863-1864).

In 2017, a series of human remains corresponding to the executed leaders of the "January Uprising" of 1863-1864 were uncovered at the Upper Castle of Vilnius (Lithuania). During the archeological excavations, 14 inhumation pits with the human remains of 21 individuals were found at the site. The subsequent identification process was carried out, including the analysis and cross-comparison of post-mortem data obtained in situ and in the lab with ante-mortem data obtained from historical archives.

Widespread resistance to anticoagulant rodenticides in Mus musculus domesticus in the city of Barcelona.

Control of rodent populations is a big challenge because of the rapid evolution of resistance to commonly used rodenticides and the collateral negative impacts that these products may have on biodiversity. Second-generation anticoagulants are very efficient but different single nucleotide polymorphisms (SNPs) in the Vkorc1 gene may confer resistance in rodents. We sequenced exons 1, 2 and 3 of the Vkorc1 gene from 111 mice (Mus musculus domesticus) captured across the city of Barcelona and found SNPs associated with resistance to first- and second-generation anticoagulants in all of them.

Effectiveness of the Modification of Sewers to Reduce the Reproduction of and in Barcelona, Spain.

Mosquitoes breeding in urban sewage infrastructure are both a source of nuisance to the local population and a public health risk, given that biting mosquitoes can transmit pathogenic organisms to humans. The increasing presence of the invasive mosquito species in European cities has further exacerbated the problems already caused by native . We tested the effectiveness of modifications to sewage structures as an alternative to the use of biocides to prevent mosquito breeding.

Automatic landmark annotation in 3D surface scans of skulls: Methodological proposal and reliability study.

Background And Objectives: Craniometric landmarks are essential in many biomedical applications, such as morphometric analysis or forensic identification. The process of locating landmarks is usually a manual and slow task, highly influenced by fatigue, skills and the experience of the practitioner. Localization errors are propagated and magnified in subsequent steps, which can result in incorrect measurements or assumptions.

Age estimation in forensic anthropology: methodological considerations about the validation studies of prediction models.

There is currently no clear consensus on how to calculate, express, and interpret the error when validating methods for age estimation in forensic anthropology. For this reason, it is likely that researchers are commonly drawing erroneous or confusing conclusions about the existence of population differences or the need to design new and increasingly complex estimation methods. In recent years, many researchers have highlighted these limitations.

Estimation of 2D to 3D dimensions and proportionality indices for facial examination.

Photo-anthropometry is a metric-based facial image comparison technique where measurements of the face are taken from an image using predetermined facial landmarks. In particular, dimensions and proportionality indices (DPIs) are compared to DPIs from another facial image. Different studies concluded that photo-anthropometric facial comparison, as it is currently practiced, is unsuitable for elimination purposes.

Intensity-based image registration using scatter search.

Objective: We present a novel intensity-based algorithm for medical image registration (IR).

Methods And Materials: The IR problem is formulated as a continuous optimization task, and our work focuses on the development of the optimization component. Our method is designed over an advanced scatter search template, and it uses a combination of restart and dynamic boundary mechanisms integrated within a multi-resolution strategy.

Biomedical image segmentation using geometric deformable models and metaheuristics.

This paper describes a hybrid level set approach for medical image segmentation. This new geometric deformable model combines region- and edge-based information with the prior shape knowledge introduced using deformable registration. Our proposal consists of two phases: training and test.