Comparison of the Hindin Proteins of Strongylocentrotus franciscanus, S. purpuratus, and Lytechinus variegatus: Sequences involved in the Species Specificity of Fertilization

Bindin is the sea urchin sperm acrosomal protein that is responsible for the species-specific adhesion of the sperm to the egg. Two new bindin cDNA sequences that contain the entire open reading frame for the binding precursor are reported: one for Strongylocentrotus franciscanus and one for Lytechinus variegatus. Both contain inverted repetitive sequences in their 3' untranslated regions, and the S. franciscanus cDNA contains an inverted repetitive sequence match between the 5' untranslated region and the coding region. The middle third of the mature bindin sequence is highly conserved in all three species, and the flanking sequences share short repeated sequences that vary in number between the species. Cross-fertilization data are reported for the species S. purpuratus, S. franciscanus, L. variegatus, and L. pictus. A barrier to cross-fertilization exists between the sympatric Strongylocentrotus species, but there is no barrier between the allopatric Lytechinus species

Monitoring COVID-19 on social media: development of an end-to-end natural language processing pipeline using a novel triage and diagnosis approach

Background: The COVID-19 pandemic has created a pressing need for integrating information from disparate sources in order to assist decision makers. Social media is important in this respect; however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. Here, we adopt a triage and diagnosis approach to analyzing social media posts using machine learning techniques for the purpose of disease detection and surveillance. We thus obtain useful prevalence and incidence statistics to identify disease symptoms and their severities, motivated by public health concerns. Objective: This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts in order to provide researchers and public health practitioners with additional information on the symptoms, severity, and prevalence of the disease rather than to provide an actionable decision at the individual level. Methods: The text processing pipeline first extracted COVID-19 symptoms and related concepts, such as severity, duration, negations, and body parts, from patients’ posts using conditional random fields. An unsupervised rule-based algorithm was then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations were subsequently used to construct 2 different vector representations of each post. These vectors were separately applied to build support vector machine learning models to triage patients into 3 categories and diagnose them for COVID-19. Results: We reported macro- and microaveraged F1 scores in the range of 71%-96% and 61%-87%, respectively, for the triage and diagnosis of COVID-19 when the models were trained on human-labeled data. Our experimental results indicated that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. In addition, we highlighted important features uncovered by our diagnostic machine learning models and compared them with the most frequent symptoms revealed in another COVID-19 data set. In particular, we found that the most important features are not always the most frequent ones. Conclusions: Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from social media natural language narratives, using a machine learning pipeline in order to provide information on the severity and prevalence of the disease for use within health surveillance systems

ACETYLCHOLINESTERASE ACTIVITY IN ESERINE-TREATED ASCIDIAN EMBRYOS

Volume: 139Start Page: 239End Page: 24