Spy: A Flexible Code Profiling Framework

Code profiling is an essential activity to increase software quality. It is commonly employed in a wide variety of tasks, such as supporting program comprehension, determining execution bottlenecks, and assessing code coverage by unit tests. Spy is an innovative framework to easily build profilers and visualize profiling information. The profiling information is obtained by inserting dedicated code before or after method execution. The gathered profiling information is structured in line with the application structure in terms of packages, classes, and methods. Spy has been instantiated on four occasions so far. We created profilers dedicated to test coverage, time execution, type feedback, and profiling evolution across version. We also integrated Spy in the Pharo IDE. Spy has been implemented in the Pharo Smalltalk programming language and is available under the MIT license.

Alternative DNA base-pairs: from efforts to expand the genetic code to potential material applications

The complementary Watson-Crick base-pairs, A:T and G:C, have long been recognized as pivotal to both the stability of the DNA double helix and replication/transcription. Recently, the replacement of the Watson-Crick base-pairs with other molecular entities has received considerable attention. In this tutorial review we highlight different approaches used to replace natural base-pairs and equip them with novel function. We also discuss the advantages that non-natural base-pairs convey with respect to practical applications.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Capture, crawl, cross: the T cell code to breach the blood-brain barriers

The central nervous system (CNS) is an immunologically privileged site to which access of circulating immune cells is tightly controlled by the endothelial blood-brain barrier (BBB; see Glossary) localized in CNS microvessels, and the epithelial blood-cerebrospinal fluid barrier (BCSFB) within the choroid plexus. As a result of the specialized structure of the CNS barriers, immune cell entry into the CNS parenchyma involves two differently regulated steps: migration of immune cells across the BBB or BCSFB into the cerebrospinal fluid (CSF)-drained spaces of the CNS, followed by progression across the glia limitans into the CNS parenchyma. With a focus on multiple sclerosis (MS) and its animal models, this review summarizes the distinct molecular mechanisms required for immune cell migration across the different CNS barriers.