Discovering information relevant to API elements using text classification

With the growing size of Application Programming Interfaces (APIs), both API usability and API learning become more challenging. API learning resources are often crucial for helping developers learn an API, but they are distributed across different documents, which makes finding the necessary information more challenging. This work focuses on discovering relevant sections of tutorials for a given API type. We approach this problem by identifying API types in an API tutorial, dividing the tutorial into small fragments and classifying them based on linguistic and structural features. The system we developed can ease information discovery for the developers who need information about a particular API type. Experiments conducted on five tutorials show that our approach is able to discover sections relevant to an API type with 0.79 average precision, 0.73 average recall, and 0.75 average F1 measure when trained and tested on the same tutorial. When trained on four tutorials and tested on a fifth tutorial the average precision is 0.84, average recall is 0.62, and the F1 measure is 0.71.Avec la taille grandissante des interfaces de programmation (API), l'aptitude àl'utilisation ainsi que la facilité d'apprentissage deviennent des préoccupations de premier ordre. La disponibilité de ressources d'apprentissage des API est de grande importance pour parvenir à developer efficacement à partir de différentes sources de documentation. Ce mémoire est consacré au problème de découverte automatique de sections pertinentes contenues dans les tutoriels des API. Nous traitons ce problème en commençant par l'identification du type d'API d'un tutoriel pour ensuite le diviser en fragments qui seront classés d'après leurs propriétés structurelles et linguistiques. Le système que nous avons développé rend le processus de découverte de sections de tutoriel beaucoup plus facile. Une évaluation de notre système a été réalisée avec cinq tutoriels et montre que notre approche peut découvrir des sections pertinentes avec une précision moyenne de 0.79, 0.73 en moyenne de rappel, et 0.75 de mesure moyenne F1 lorsque entraîné ettesté pour le même tutoriel. Lorsqu'entraîné depuis quatre tutoriels et testé dans avec le cinquième, nous obtenons 0.84 de précision moyenne, 0.62 de moyenne de rappel, et finalement 0.71 de mesure F

Dataset

<p>Dataset for "Discovering Information Explaining API Types Using Text Classification"</p

The Involvement of Arginase and Nitric Oxide Synthase in Breast Cancer Development: Arginase and NO Synthase as Therapeutic Targets in Cancer

It is well established that, during development of malignancies, metabolic changes occur, including alterations of enzyme activities and isoenzyme expression. Arginase and nitric oxide (NO) synthase (NOS) are two of those enzymes considered to be involved in tumorigenesis. The goal of this article was to study the involvement of arginase and NOS in the development of different stages of breast cancer. Our results have shown that human serum arginase activity and NO (resp., and NOS activity) and polyamines quantities increased in parallel with cancer stage progression and decreased after neoadjuvant chemotherapy. For breast cancer, the only isoenzyme of arginase expressed in serum before and after chemotherapy was in a cationic form. The data of Lineweaver-Burk plot with a Km value of 2 mM was calculated, which is characteristic for human liver type isoform of arginase. During electrophoresis at pH 8.9, the enzyme exhibited high electrophoretic mobility and was detected near the anode. The presented results demonstrated that arginase in human serum with breast cancer and after chemotherapy is not polymorphic. We suggest that arginase and NOS inhibition has antitumor effects on cancer development, as it can inhibit polyamines and NO levels, a precursor of cancer cell proliferation, metastasis, and tumor angiogenesis

Kinetics of Anti-Nucleocapsid IgG Response in COVID-19 Immunocompetent Convalescent Patients

International audienceAbstract The comprehension of a long-term humoral immune response against SARS-CoV-2 can shed light on the treatment and vaccination strategies of COVID-19 disease, improving the knowledge about this virus infection and/or re-infection. We assessed the IgG antibodies against SARS-CoV-2 nucleocapsid (N) protein (anti-SARS-CoV-2 (N) IgG) in 1441 COVID-19 convalescent patients within 15~months longitudinal study from middle-developed country. The main inclusion criteria was positive RT\textendash PCR result on nasopharyngeal swab samples at least one month before antibody testing and absence of any induced or inherited immunodeficiency. 92.7% of convalescent patients' serum contained anti-SARS-CoV-2 (N) IgG and only 1.3% of patients had a delayed antibody response. In the majority of convalescent patients' the durability of antibodies lasted more than one year. The kinetics of anti-SARS-CoV-2 (N) IgG took a bell-shaped character\textemdash increased first 25\textendash 30~weeks, then started to decrease, but were still detectable for more than 15~months. We found that on the one hand anti-SARS-CoV-2 humoral response level correlates with disease severity, on the other, in particular, the level of peak antibodies correlates with age\textemdash older patients develop more robust humoral response regardless of sex, disease severity and BMI