Source Code Comment Classification Artificial Intelligence

Source code comment classification is an important problem for future machine learning solutions. In particular, supervised machine learning solutions that have largely subjective data labels but are difficult to obtain the labels for. Machine learning problems are problems largely because of a lack of data. In machine learning solutions, it is better to have a large amount of mediocre data than it is to have a small amount of good data. While the mediocre data might not produce the best accuracy, it produces the best results because there is much more to learn from the problem. In this project, data was collected from student comment code in computer science classes. This data was then sorted based on various tools in order to create automated source code classification. Various data categorization and sorting methods were explored, ultimately resulting in a process where assigned letter grade was used as a sorting label. Using python, CommentLabeler, and SortAndUnique tools were developed in order to automate the manual source code labeling process. State retention and error checking were also features that were added to streamline the process further. The most important takeaway from this experience was that the amount of data is much more important than quality. In fact, mediocre data will provide better results with regard to machine learning because there is room for improvement and it proves machine learning as a solution

Bacterial vaginosis biofilms: challenges to current therapies and emerging solutions

Bacterial vaginosis (BV) is the most common genital tract infection in women during their reproductive years and it has been associated with serious health complications, such as preterm delivery and acquisition or transmission of several sexually transmitted agents. BV is characterized by a reduction of beneficial lactobacilli and a significant increase in number of anaerobic bacteria, including Gardnerella vaginalis, Atopobium vaginae, Mobiluncus spp., Bacteroides spp. and Prevotella spp.. Being polymicrobial in nature, BV etiology remains unclear. However, it is certain that BV involves the presence of a thick vaginal multi-species biofilm, where G. vaginalis is the predominant species. Similar to what happens in many other biofilm-related infections, standard antibiotics, like metronidazole, are unable to fully eradicate the vaginal biofilm, which can explain the high recurrence rates of BV. Furthermore, antibiotic therapy can also cause a negative impact on the healthy vaginal microflora. These issues sparked the interest in developing alternative therapeutic strategies. This review provides a quick synopsis of the currently approved and available antibiotics for BV treatment while presenting an overview of novel strategies that are being explored for the treatment of this disorder, with special focus on natural compounds that are able to overcome biofilm-associated antibiotic resistance.Research on BV biofilms in NC laboratory is supported by funding from the Fundação para a Ciência e a Tecnologia (FCT) strategic project of unit UID/BIO/04469/2013. DM and JC acknowledge the FCT fellowships SFRH/BD/87569/2012 and SFRH/BD/93963/2013 respectively. NC is an Investigador FCT

Discovery of Peptide Drugs from Natural Sources

All living organisms have their own arsenal of peptides that are responsible for vital biological functions. With an outstanding diversity of structures and modes of action, peptides can represent drug candidates in their natural form or serve as leads for the design of modified peptides. Some peptides extracted from natural sources are currently being used as therapeutics, whereas others are in the product pipelines of pharmaceutical companies. Nevertheless, a more general application of peptides has been delayed due to either real or perceived shortcomings in stability and bioavailability. Several chemical approaches are presently available to circumvent these potential shortcomings. This chapter emphasizes the remarkable range of applications and receptor specificities that are associated with peptides isolated from natural sources, and describes how knowledge from different disciplines is accelerating the application of peptide-based therapies