Micro-CT Imaging of RGD-Conjugated Gold Nanorods Targeting Tumor In Vivo

Gold nanomaterials as computed tomography (CT) contrast agents at lower X-ray dosage to get a higher contrast have advantages of longer imaging time and lower toxic side effects compared to current contrast agents. As a receptor for Cyclo (Arg-Gly-Asp-D-Phe-Lys) (RGD) peptide, integrin αvβ3 is overexpressed on some tumor cells and tumor neovasculature. In this paper, we conjugated the RGD peptide on the surface of gold nanorods (AuNRs), designated as RGD-AuNRs, a promising candidate in applications such as tumor targeting and imaging capability for micro-CT imaging. Integrin αvβ3-positive U87 cells and integrin αvβ3-negative HT-29 cells were chosen to establish animal models relatedly and then texted the tumor targeting ability and imaging capability of RGD-AuNRs in vitro and in vivo. The MTT assay and stability measurement showed that RGD-conjugation eliminated their cytotoxicity and improved their biocompatibility and stability. Dark-field imaging of U87 cells and HT-29 cells testified the binding affinities and uptake abilities of RGD-AuNRs, and the results showed that RGD-AuNRs were more specifical to U87 cells. The enhanced micro-CT imaging contrast of intramuscular and subcutaneous injection illustrated the feasibility of RGD-AuNRs to be contrast agents. Furthermore, the micro-CT imaging of targeting U87 and HT-29 tumor models verified the targeting abilities of RGD-AuNRs

Clinical skin lesion diagnosis using representations inspired by dermatologist criteria

The skin is the largest organ in human body. Around 30%-70% of individuals worldwide have skin related health problems, for whom effective and efficient diagnosis is necessary. Recently, computer aided diagnosis (CAD) systems have been successfully applied to the recognition of skin cancers in dermatoscopic images. However, little work has concentrated on the commonly encountered skin diseases in clinical images captured by easily-accessed cameras or mobile phones. Meanwhile, for a CAD system, the representations of skin lesions are required to be understandable for dermatologists so that the predictions are convincing. To address this problem, we present effective representations inspired by the accepted dermatological criteria for diagnosing clinical skin lesions. We demonstrate that the dermatological criteria are highly correlated with measurable visual components. Accordingly, we design six medical representations considering different criteria for the recognition of skin lesions, and construct a diagnosis system for clinical skin disease images. Experimental results show that the proposed medical representations can not only capture the manifestations of skin lesions effectively, and consistently with the dermatological criteria, but also improve the prediction performance with respect to the state-of-the-art methods based on uninterpretable features

Alice Benchmarks: Connecting Real World Object Re-Identification with the Synthetic

For object re-identification (re-ID), learning from synthetic data has become a promising strategy to cheaply acquire large-scale annotated datasets and effective models, with few privacy concerns. Many interesting research problems arise from this strategy, e.g., how to reduce the domain gap between synthetic source and real-world target. To facilitate developing more new approaches in learning from synthetic data, we introduce the Alice benchmarks, large-scale datasets providing benchmarks as well as evaluation protocols to the research community. Within the Alice benchmarks, two object re-ID tasks are offered: person and vehicle re-ID. We collected and annotated two challenging real-world target datasets: AlicePerson and AliceVehicle, captured under various illuminations, image resolutions, etc. As an important feature of our real target, the clusterability of its training set is not manually guaranteed to make it closer to a real domain adaptation test scenario. Correspondingly, we reuse existing PersonX and VehicleX as synthetic source domains. The primary goal is to train models from synthetic data that can work effectively in the real world. In this paper, we detail the settings of Alice benchmarks, provide an analysis of existing commonly-used domain adaptation methods, and discuss some interesting future directions. An online server will be set up for the community to evaluate methods conveniently and fairly.Comment: 9 pages, 4 figures, 4 table

Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?

Hand-crafted image quality metrics, such as PSNR and SSIM, are commonly used to evaluate model privacy risk under reconstruction attacks. Under these metrics, reconstructed images that are determined to resemble the original one generally indicate more privacy leakage. Images determined as overall dissimilar, on the other hand, indicate higher robustness against attack. However, there is no guarantee that these metrics well reflect human opinions, which, as a judgement for model privacy leakage, are more trustworthy. In this paper, we comprehensively study the faithfulness of these hand-crafted metrics to human perception of privacy information from the reconstructed images. On 5 datasets ranging from natural images, faces, to fine-grained classes, we use 4 existing attack methods to reconstruct images from many different classification models and, for each reconstructed image, we ask multiple human annotators to assess whether this image is recognizable. Our studies reveal that the hand-crafted metrics only have a weak correlation with the human evaluation of privacy leakage and that even these metrics themselves often contradict each other. These observations suggest risks of current metrics in the community. To address this potential risk, we propose a learning-based measure called SemSim to evaluate the Semantic Similarity between the original and reconstructed images. SemSim is trained with a standard triplet loss, using an original image as an anchor, one of its recognizable reconstructed images as a positive sample, and an unrecognizable one as a negative. By training on human annotations, SemSim exhibits a greater reflection of privacy leakage on the semantic level. We show that SemSim has a significantly higher correlation with human judgment compared with existing metrics. Moreover, this strong correlation generalizes to unseen datasets, models and attack methods.Comment: 15 pages, 9 figures and 3 table

Exploring Multi-Programming-Language Commits and Their Impacts on Software Quality: An Empirical Study on Apache Projects

Context: Modern software systems (e.g., Apache Spark) are usually written in multiple programming languages (PLs). There is little understanding on the phenomenon of multi-programming-language commits (MPLCs), which involve modified source files written in multiple PLs. Objective: This work aims to explore MPLCs and their impacts on development difficulty and software quality. Methods: We performed an empirical study on eighteen non-trivial Apache projects with 197,566 commits. Results: (1) the most commonly used PL combination consists of all the four PLs, i.e., C/C++, Java, JavaScript, and Python; (2) 9% of the commits from all the projects are MPLCs, and the proportion of MPLCs in 83% of the projects goes to a relatively stable level; (3) more than 90% of the MPLCs from all the projects involve source files in two PLs; (4) the change complexity of MPLCs is significantly higher than that of non-MPLCs; (5) issues fixed in MPLCs take significantly longer to be resolved than issues fixed in non-MPLCs in 89% of the projects; (6) MPLCs do not show significant effects on issue reopen; (7) source files undergoing MPLCs tend to be more bug-prone; and (8) MPLCs introduce more bugs than non-MPLCs. Conclusions: MPLCs are related to increased development difficulty and decreased software quality.Comment: Preprint accepted for publication in Journal of Systems and Software, 2022. arXiv admin note: substantial text overlap with arXiv:2103.1169

Technical Debt Management in OSS Projects: An Empirical Study on GitHub

Technical debt (TD) refers to delayed tasks and immature artifacts that may bring short-term benefits but incur extra costs of change during maintenance and evolution in the long term. TD has been extensively studied in the past decade, and numerous open source software (OSS) projects were used to explore specific aspects of TD and validate various approaches for TD management (TDM). However, there still lacks a comprehensive understanding on the practice of TDM in OSS development, which penetrates the OSS community's perception of the TD concept and how TD is managed in OSS development. To this end, we conducted an empirical study on the whole GitHub to explore the adoption and execution of TDM based on issues in OSS projects. We collected 35,278 issues labeled as TD (TD issues) distributed over 3,598 repositories in total from the issue tracking system of GitHub between 2009 and 2020. The findings are that: (1) the OSS community is embracing the TD concept; (2) the analysis of TD instances shows that TD may affect both internal and external quality of software systems; (3) only one TD issue was identified in 31.1% of the repositories and all TD issues were identified by only one developer in 69.0% of the repositories; (4) TDM was ignored in 27.3% of the repositories after TD issues were identified; and (5) among the repositories with TD labels, 32.9% have abandoned TDM while only 8.2% adopt TDM as a consistent practice. These findings provide valuable insights for practitioners in TDM and promising research directions for further investigation.Comment: 15 pages, 8 images, 10 tables, Manuscript submitted to a Journal (2022