We present here a multisource approach that takes advantage of several disciplines to address a taxonomic issue. A triatomine related to Rhodnius robustus Larrousse, 1927 was recently found in the state of Rondônia, Brazil. The name Rhodnius montenegrensis n. sp. is suggested because it was found in the municipality of Monte Negro. The main differences between these two species can be detected in the female and male genitalia, but there are also noticeable differences in their eggs. Molecular analysis using PCR-RFLP technique and Bayesian inferences based on a fragment of the Cytochrome b (Cyt b) gene corroborated the morphological findings. We used this integrative approach to address the taxonomic decision for a new Rhodnius species and its relationship with others of this genus. Results obtained herein stress that morphology must be used as the major approach for obtaining phenotypic information, and molecular data should be taken as a complementary tool.

Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method for embryo analysis that is more robust and reliable than standard methods. Bovine blastocysts produced in vitro were classified as grade 1 (excellent or good), 2 (fair), or 3 (poor) by three experienced embryologists according to the International Embryo Technology Society (IETS) standard. The images (n = 482) were subjected to automatic feature extraction, and the results were used as input for a supervised learning process. One part of the dataset (15%) was used for a blind test posterior to the fitting, for which the system had an accuracy of 76.4%. Interestingly, when the same embryologists evaluated a sub-sample (10%) of the dataset, there was only 54.0% agreement with the standard (mode for grades). However, when using the ANN to assess this sub-sample, there was 87.5% agreement with the modal values obtained by the evaluators. The presented methodology is covered by National Institute of Industrial Property (INPI) and World Intellectual Property Organization (WIPO) patents and is currently undergoing a commercial evaluation of its feasibility.

A colony was formed from eggs of a Rhodnius sp. female collected in Taquarussu, Mato Grosso do Sul, Brazil, and its specimens were used to describe R. taquarussuensis sp. n. This species is similar to R. neglectus, but distinct characters were observed on the head, thorax, abdomen, female external genitalia and male genitalia. Chromosomal differences between the two species were also established.

Morphological embryo classification is of great importance for many laboratory techniques, from basic research to the ones applied to assisted reproductive technology. However, the standard classification method for both human and cattle embryos, is based on quality parameters that reflect the overall morphological quality of the embryo in cattle, or the quality of the individual embryonic structures, more relevant in human embryo classification. This assessment method is biased by the subjectivity of the evaluator and even though several guidelines exist to standardize the classification, it is not a method capable of giving reliable and trustworthy results. Latest approaches for the improvement of quality assessment include the use of data from cellular metabolism, a new morphological grading system, development kinetics and cleavage symmetry, embryo cell biopsy followed by pre-implantation genetic diagnosis, zona pellucida birefringence, ion release by the embryo cells and so forth. Nowadays there exists a great need for evaluation methods that are practical and non-invasive while being accurate and objective. A method along these lines would be of great importance to embryo evaluation by embryologists, clinicians and other professionals who work with assisted reproductive technology. Several techniques shows promising results in this sense, one being the use of digital images of the embryo as basis for features extraction and classification by means of artificial intelligence techniques (as genetic algorithms and artificial neural networks). This process has the potential to become an accurate and objective standard for embryo quality assessment.