In recent years, an increasing number of cabled Fixed Underwater Observatories (FUOs) have been deployed, many of them equipped with digital cameras recording high-resolution digital image time series for a given period. The manual extraction of quantitative information from these data regarding resident species is necessary to link the image time series information to data from other sensors but requires computational support to overcome the bottleneck problem in manual analysis. As a priori knowledge about the objects of interest in the images is almost never available, computational methods are required that are not dependent on the posterior availability of a large training data set of annotated images. In this paper, we propose a new strategy for collecting and using training data for machine learning-based observatory image interpretation much more efficiently. The method combines the training efficiency of a special active learning procedure with the advantages of deep learning feature representations. The method is tested on two highly disparate data sets. In our experiments, we can show that the proposed method ALMI achieves on one data set a classification accuracy A > 90% with less than N = 258 data samples and A > 80% after N = 150 iterations, i.e., training samples, on the other data set outperforming the reference method regarding accuracy and training data required.Long-read sequencing technologies have opened up new avenues of research on the mosquito genome biology, enabling scientists to better understand the remarkable abilities of vectors for transmitting pathogens.  https://www.selleckchem.com/products/fht-1015.html  Although new genome mapping technologies such as Hi-C scaffolding and optical mapping may significantly improve the quality of genomes, only cytogenetic mapping, with the help of fluorescence in situ hybridization (FISH), connects genomic scaffolds to a particular chromosome and chromosome band. This mapping approach is important for creating and validating chromosome-scale genome assemblies for mosquitoes with repeat-rich genomes, which can potentially be misassembled. In this study, we describe a new gene-based physical mapping approach that was optimized using the newly assembled Aedes albopictus genome, which is enriched with transposable elements. To avoid amplification of the repetitive DNA, 15 protein-coding gene transcripts were used for the probe design. Instead of using genomic DNA, complementary DNA was utilized as a template for development of the PCR-amplified probes for FISH. All probes were successfully amplified and mapped to specific chromosome bands. The genome-unique probes allowed to perform unambiguous mapping of genomic scaffolds to chromosome regions. The method described in detail here can be used for physical genome mapping in other insects.Autism Spectrum Disorder (ASD) remains one of the most detrimental neurodevelopmental conditions in society today. Common symptoms include diminished social and communication ability. Investigations on autism etiology remain largely ambiguous. Previous studies have highlighted exposure to lead (Pb) may play a role in ASD. In addition, lead has been shown to be one of the most prevalent metal exposures associated with neurological deficits. A semi-systematic review was conducted using public databases in order to evaluate the extent of lead's role in the etiology of autism. This review examines the relationship between autistic comorbid symptoms-such as deterioration in intelligence scores, memory, language ability, and social interaction-and lead exposure. Specifically, the mechanisms of action of lead exposure, including changes within the cholinergic, dopaminergic, glutamatergic, gamma aminobutyric acid (GABA)ergic systems, are discussed. The goal of this review is to help illustrate the connections between lead's mechanistic interference and the possible furthering of the comorbidities of ASD. Considerations of the current data and trends suggest a potential strong role for lead in ASD.Microglia are immune brain cells involved in neuroinflammation. They express a lot of proteins on their surface such as receptors that can be activated by mediators released in the microglial environment. Among these receptors, purinergic receptor expression could be modified depending on the activation status of microglia. In this review, we focus on P2Y receptors and more specifically on P2RY12 that is involved in microglial motility and migration, the first step of neuroinflammation process. We describe the purinergic receptor families, P2RY12 structure, expression and physiological functions. The pharmacological and genetic tools for studying this receptor are detailed thereafter. Last but not least, we report the contribution of microglial P2RY12 to neuroinflammation in acute and chronic brain pathologies in order to better understand P2RY12 microglial role.Common software vulnerabilities can result in severe security breaches, financial losses, and reputation deterioration and require research effort to improve software security. The acceleration of the software production cycle, limited testing resources, and the lack of security expertise among programmers require the identification of efficient software vulnerability predictors to highlight the system components on which testing should be focused. Although static code analyzers are often used to improve software quality together with machine learning and data mining for software vulnerability prediction, the work regarding the selection and evaluation of different types of relevant vulnerability features is still limited. Thus, in this paper, we examine features generated by SonarQube and CCCC tools, to identify those that can be used for software vulnerability prediction. We investigate the suitability of thirty-three different features to train thirteen distinct machine learning algorithms to design vulnerability predictors and identify the most relevant features that should be used for training. Our evaluation is based on a comprehensive feature selection process based on the correlation analysis of the features, together with four well-known feature selection techniques. Our experiments, using a large publicly available dataset, facilitate the evaluation and result in the identification of small, but efficient sets of features for software vulnerability prediction.