MOTIVATION: Single-cell and spatial transcriptomics provide unprecedented insight into diseases. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing and discovery. Multiple databases attempt to connect human cellular transcriptional responses to small molecules for use in transcriptome-based drug discovery efforts. However, preclinical research often requires in vivo experiments in non-human species, which makes utilizing such valuable resources difficult. To facilitate both human orthologous conversion of non-human transcriptomes and the application of pharmacotranscriptomic databases to pre-clinical research models, we introduce OrthologAL. OrthologAL interfaces with BioMart to access different gene sets from the Ensembl database, allowing for ortholog conversion without the need for user-generated code. RESULTS: Researchers can input their single-cell or other high-dimensional gene expression data from any species as a Seurat object, and OrthologAL will output a human ortholog-converted Seurat object for download and use. To demonstrate the utility of this application, we tested OrthologAL using single-cell, single-nuclei, and spatial transcriptomic data derived from common preclinical models, including patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury. OrthologAL can convert these data types efficiently to that of corresponding orthologs while preserving the dimensional architecture of the original non-human expression data. OrthologAL will be broadly useful for the simple conversion of Seurat objects and for applying preclinical, high-dimensional transcriptomics data to functional human-derived small molecule predictions. AVAILABILITY AND IMPLEMENTATION: OrthologAL is available for download as an R package with functions to launch the Shiny GUI at https://github.com/AyadLab/OrthologAL or via Zenodo at https://doi.org/10.5281/zenodo.15225041. The medulloblastoma single-cell transcriptomics data were downloaded from the NCBI Gene Expression Omnibus with the identifier GSE129730. 10X Visium data of medulloblastoma PDX mouse models from Vo et al. were acquired by contacting the authors, and the raw data are available from ArrayExpress under the identifier E-MTAB-11720. The single-cell and single-nuclei transcriptomics data of rat and mouse spinal-cord injury were acquired from the Gene Expression Omnibus under the identifiers GSE213240 and GSE234774.

A summary of a user's Internet activities, such as web visits, can closely reflect their interests and preferences. However, automating the summarization process is not trivial as it should strike a good balance between generality and specificity, while there is no gold standard for doing so. In our approach to summarizing user information, we introduce two scoring mechanisms that cooperatively optimize for polarizing criteria. Having mapped user activity information onto a category tree, the scoring mechanisms highlight the most representative tree node; the node provides an aggregated view, i.e., a summary, of the activities most representative of the user. We evaluate our approach by summarizing web activity on the network of a large cellular service provider to devise interests of individual users as well as user groups.

A Sybil attack can inject many forged identities (called Sybils) to subvert a target system. Because of the severe damage that Sybil attacks can cause to a wide range of networking applications, there has been a proliferation of Sybil defense schemes. Of particular attention are those that explore the online social networks (OSNs) of users in a victim system in different ways. Unfortunately, while effective Sybil defense solutions are urgently needed, it is unclear how effective these OSN-based solutions are under different contexts. For example, all current approaches have focused on a common, classical scenario where it is difficult for an attacker to link Sybils with honest users and create attack edges; however, researchers have found recently that a modern scenario also becomes typical where an attacker can employ simple strategies to obtain many attack edges. In this work we analyze the state of OSN-based Sybil defenses. Our objective is not to design yet another solution, but rather to thoroughly analyze, measure, and compare how well or inadequate the well-known existing OSN-based approaches perform under both the classical scenario and the modern scenario. Although these approaches mostly perform well under the classical scenario, we find that under the modern scenario they are vulnerable to Sybil attacks. As shown in our quantitative analysis, very often a Sybil only needs a handful of attack edges to disguise itself as a benign node, and there is only a limited success in tolerating Sybils. Our study further points to capabilities a new solution must possess; in particular, in defense against Sybils under the modern scenario, we anticipate a new approach that enriches the structure of a social graph with more information about the relations between its users can work more effectively.

A Sybil attack can inject many forged identities (called Sybils) to subvert a target system. Among various defense approaches, of particular attention are those that explore the online social networks (OSNs) of users in a target system to detect or tolerate Sybil nodes. Albeit different in their working principle, all these approaches assume it is difficult for an attacker to create attack edges to connect Sybils with honest users. However, researchers have found that an attacker can employ simple strategies to obtain many attack edges. In this work we revisit the state-of-the-art, OSN-based Sybil defenses, and point out their strengths and weaknesses due to the impact of the new properties. We find these defense approaches are vulnerable to attackers under the new scenario, and in many cases a Sybil node only needs to obtain a handful of attack edges to disguise itself as a benign node.