FUNCTIONAL POLYGYNY, AGONISTIC INTERACTIONS AND REPRODUCTIVE DOMINANCE IN THE NEOTROPICAL ANT ODONTOMACHUS-CHELIFER (HYMENOPTERA, FORMICIDAE, PONERINAE)

Intracolony aggression among dealated queens of the ponerine ant Odontomachus chelifer leads to a dominance order within the colony. Behavioral domination between queens entails an array of stereotyped displays which may escalate from vigorous antennation bouts to full mandibular strikes. In extreme situations a dominant queen may also suspend her subordinate opponent, who remains in pupal posture while being lifted up. As a rule the individual initiating a contest normally wins it. Subordinate queens may assume a crouching posture at the approach of a dominant nestmate from behind, a fact suggesting that chemical cues may also play a role in the establishment of the dominance order. Behavioral performances during domination contests and the rank position of different dealated queens correlated well with the data on individual egg production, ovarian development and other parameters of division of labor within the colony. Therefore, highly-ranked queens laid more eggs, had better developed ovaries and engaged less frequently in foraging activities outside the nest. Inseminated queens occupied the top positions in the dominance structure of the colony, and accounted for most of the aggressive interactions recorded within the nest tubes. Aggression toward egg-laying queens and the destruction of newly-laid eggs were conspicuous behavioral traits in the reproductive dominance of the O. chelifer colony. High ranking dealated queens were also the ones more frequently seen attacking alate females. The latter were observed to lay eggs, and some of them had developed ovaries. Our results with Odontomachus chelifer are in accordance with the data obtained elsewhere for other ponerine ants, and provide the first demonstration of a dominance structure linked to reproductive status among queens in a functionally polygynous ant colony.91213414

Reverse image search for scientific data within and beyond the visible spectrum

The explosion in the rate, quality and diversity of image acquisition instruments has propelled the development of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper introduces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR, a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released

Hierarchical median narrow band for level set segmentation of cervical cell nuclei

This paper presents a novel hierarchical nuclei segmentation algorithm for isolated and overlapping cervical cells based on a narrow band level set implementation. Our method applies a new multiscale analysis algorithm to estimate the number of clusters in each image region containing cells, which turns into the input to a narrow band level set algorithm. We assess the nuclei segmentation results on three public cervical cell image databases. Overall, our segmentation method outperformed six state-of-the-art methods concerning the number of correctly segmented nuclei and the Dice coefficient reached values equal to or higher than 0.90. We also carried out classification experiments using features extracted from our segmentation results and the proposed pipeline achieved the highest average accuracy values equal to 0.89 and 0.77 for two-class and three-class problems, respectively. These results demonstrated the suitability of the proposed segmentation algorithm to integrate decision support systems for cervical cell screening

A hierarchical feature-based methodology to perform cervical cancer classification

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visu-ally by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods