Automated analysis for auto-generated build systems

Software build systems are crucial for software development as they translate the source code and resources into a deliverable. Prior work has identified that build systems account for 9% of software systems. However, their maintenance imposes a 36% overhead on software development. This overhead stems from the unique and hard to comprehend the nature of build systems. When executed, the build system is evaluated into a dependency-graph that captures how the system’s artifacts relate to each other. The graph generated depends on the selected build configurations. This graph is then traversed to perform the build. Prior work has emphasized the need for analysis support to tackle the challenges of evolving and maintaining build systems. In this thesis, we tackle three challenges associated with the maintenance and evolution of build systems. As the build system evolves, it’s not trivial to understand the impact of build code changes on its semantics. To tackle this, we propose a build code differencing technique to identify the semantic changes between two versions of a given build system. This would provide visibility on how the build system is evolving along with the software system. The second challenge we tackle is localizing faults within build systems. Build-time failures occur after the build code has been evaluated, and during the traversal of the dependency graph, it’s challenging to trace back the failure from the graph back to its root cause in the build system code. To this end, we propose a novel approach to localize faults in build code. For a given build failure, it returns a ranked list of statements in the build code that are suspected of causing the failure. This would aid in reducing the overhead of debugging and root causing build failures. The third challenge is to extract knowledge from build systems for analysis purposes. We propose an approach to extract the presence conditions of source code files from within the build system. This aims to support configuration aware analysis of configurable source code influenced by the build system. We then proceed to propose a foundation for developers to create analysis techniques to help them understand, maintain, and migrate their generator-based build system. We illustrate the use of the platform with two approaches: one to help developers better understand their build systems and another to detect build smells to improve the build code quality. To evaluate our work, we implement our proposed approaches against the widely used GNU build suite. Then, we use open-source projects to evaluate each of the approaches

Acute Stress Disorders Among Jordanian Adolescents After Watching Gaza News Footage on Social Media

Dua’a Al-Maghaireh,1 Najah Sami Shawish,2 Khitam Alsaqer,1 Mariam Kawafha,3 Heidar Sultan Sheyab,4 Rama Ashraf Al Mushasha,5 Abedelkder Al Kofahi6 1Faculty of Nursing, Irbid National University, Irbid, Jordan; 2Faculty of Nursing, Al-Zaytoonah University of Jordan, Amman, Jordan; 3Faculty of Nursing, Philadelphia University, Amman, Jordan; 4College of Medicine, Jordan University of Science and Technology, Irbid, Jordan; 5College of Medicine, Yarmouk University, Irbid, Jordan; 6Irbid University College, Al Balqa Applied University, Irbid JordanCorrespondence: Dua’a Al-Maghaireh, Faculty of Nursing, Irbid National University, Irbid, Jordan, Email [email protected]: The ongoing conflict in Gaza has led to a surge in acute stress among individuals who are exposed to distressing images and videos daily via social media.Aim: This study aimed to examine the impact of watching Gaza news footage on social media among Jordanian adolescents, and explore the experiences of watching Gaza news footage on social media from the perspective of adolescents.Methods: An explanatory mixed methods design was conducted from 10/10/2023 until 6/11/2023, undertaken at two government high schools in Jordan. The Perceived Stress Scale was used to survey 180 Jordanian students who watched Gaza news footage on social media. Then the students who had high stress levels were interviewed.Results: 180 students participated in this study, more than half of them were male (52.2%). The quantitative findings showed that the students experienced high stress levels, with a mean score of 3.78 (SD = 1.24). 70% of students reported high levels of stress, the amount of time spent watching news footage about the Gaza attack on social media each day, and the presence of social support from family or peers are significantly linked to stress levels (p < 0.05). The qualitative findings revealed the following themes: Extreme Emotional Responses, Sources of Stressors and Impact of Stress Extreme Emotional Responses, Sources of Stressors and Impact of Stress.Conclusion: Study findings revealed high stress levels among adolescents after watching Gaza news on social media, highlighting the need for interventions in the context of the three major themes revealed in the study.Keywords: Gaza, acute, stress, social media, news footage, adolescent

Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

NetMets: software for quantifying and visualizing errors in biological network segmentation

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization

Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutaminemediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington’s Disease (HD) model.National Institutes of Health (U.S.) (Grant

Computational prediction of neural progenitor cell fates

Understanding how stem and progenitor cells choose between alternative cell fates is a major challenge in developmental biology. Efforts to tackle this problem have been hampered by the scarcity of markers that can be used to predict cell division outcomes. Here we present a computational method, based on algorithmic information theory, to analyze dynamic features of living cells over time. Using this method, we asked whether rat retinal progenitor cells (RPCs) display characteristic phenotypes before undergoing mitosis that could foretell their fate. We predicted whether RPCs will undergo a self-renewing or terminal division with 99% accuracy, or whether they will produce two photoreceptors or another combination of offspring with 87% accuracy. Our implementation can segment, track and generate predictions for 40 cells simultaneously on a standard computer at 5 min per frame. This method could be used to isolate cell populations with specific developmental potential, enabling previously impossible investigations.The computational aspects of this work were supported by the Center for Subsurface Sensing and Imaging Systems (NSF Grant EEC-9986821), by the Rensselaer Polytechnic Institute and by the University of Wisconsin-Milwaukee. This work was supported by grants from the Canadian Institutes of Health Research and the Foundation Fighting Blindness – Canada (to M.C). M.C. is a CIHR New Investigator and a W.K. Stell Scholar of the Foundation Fighting Blindness – Canada