MOTIVATION: The Killer-cell Immunoglobulin-like Receptor (KIR) is a highly polymorphic region in the human genome, associated with autoimmune diseases and organ transplantation. The sequences of KIR genes are highly similar among star alleles as well as in between individual genes, with the copy number of each KIR gene typically ranging from 0 to 4. In this study, we introduce Graph-KIR, a tool designed to estimate gene copy numbers and predict full-resolution (7-digit, encompassing both coding and non-coding sequence variations) from a whole genome sequencing (WGS) sample. RESULTS: Graph-KIR is capable of independently typing KIR alleles per sample with no reliance on the distribution of any framework gene in a cohort. In a set of 100 simulated samples, Graph-KIR demonstrated 99.2% accuracy in copy number estimation and high F1-score of allele typing: 91.79% at 7-digit resolution, 97.37% at 5-digit resolution, and 97.11% at 3-digit resolution. Graph-KIR outperforms existing tools such as Geny (96.39% F1-score), PING's WGS version (92.77% F1-score), and T1K (90.44% F1-score) at 5-digit resolution. By analyzing the results on 44 HPRC samples, Graph-KIR achieves better F1-score than Geny and PING at 7-digit resolution. The release of Graph-KIR adds another valuable tool to assist users in accurately estimating copy numbers and calling alleles of KIR genes from WGS samples. AVAILABILITY AND IMPLEMENTATION: The Graph-KIR and paper-related pipeline codes are available at https://github.com/linnil1/KIR_graph.

We study the Patrol Security Game (PSG), a robotic patrolling problem formulated as an extensive-form Stackelberg game, in which the attacker strategically selects the timing, location, and duration of an attack. The defender’s goal is to compute an infinite-horizon patrolling policy that minimizes the attacker’s expected payoff. By restricting the defender’s strategy to a time-homogeneous first-order Markov chain, we show that PSG can be reformulated as a combinatorial minimax problem. We prove that the optimal strategy under zero-penalty scenarios corresponds to minimizing either the expected hitting time or return time, depending on the attacker’s visibility model. These optimal policies are closed-form and can be computed efficiently. On the other hand, in high-penalty cases, we observe that the patrolling schedule with high randomness can minimize the attacker’s expected gain. However, in general, the minimax objective becomes non-convex. To address this, we introduce a bi-criteria optimization framework that jointly considers the expected maximum reward (EMR) and entropy rate of the patrolling policy. We propose three graph-based algorithms and a deep reinforcement learning model to efficiently balance these two objectives. Each algorithm demonstrates distinct strengths under different configurations, such as varying penalty scales and cost function settings. The extensive experiments on both synthetic and real-world crime datasets validate the effectiveness of our approaches, demonstrating superior performance and scalability compared to state-of-the-art baselines.

Recently, Gupta and Chaudhari proposed an anonymous two factor authentication protocol for roaming service in global mobile networks. They claimed that their scheme could not only ensure strong user anonymity, mutual authentication and perfect forward secrecy but also resist desynchronization attack, password guessing attack, replay attack, and insider attack. After analyzing their scheme, we find that it suffers from some flaws. First, the foreign agent cannot determine who the home agent is and whether the received request is for itself or not. Second, some operation cannot be executed by the home agent to record the number of authentication failure. Third, the foreign agent cannot determine whether the message received sent by the home agent is for itself or not. Fourth, a malicious user can mount parallel attack to obtain the unauthorized service. In this paper, we will show how these flaws threaten Gupta and Chaudhari’s protocol.

BACKGROUND: Alternative RNA splicing greatly increases proteome diversity and thereby contribute to species- or tissue-specific functions. The possibility to study alternative splicing (AS) events on a genomic scale using splicing-sensitive microarrays, including the Affymetrix GeneChip Exon 1.0 ST microarray (exon array), has appeared very recently. However, the application of this new technology is hindered by the lack of free and user-friendly software devoted to these novel platforms. RESULTS: In this study we present a Java-based freeware, easyExon http://microarray.ym.edu.tw/easyexon, to process, filtrate and visualize exon array data with an analysis pipeline. This tool implements the most commonly used probeset summarization methods as well as AS-orientated filtration algorithms, e.g. MIDAS and PAC, for the detection of alternative splicing events. We include a biological filtration function according to GO terms, and provide a module to visualize and interpret the selected exons and transcripts. Furthermore, easyExon can integrate with other related programs, such as Integrate Genome Browser (IGB) and Affymetrix Power Tools (APT), to make the whole analysis more comprehensive. We applied easyExon on a public accessible colon cancer dataset as an example to illustrate the analysis pipeline of this tool. CONCLUSION: EasyExon can efficiently process and analyze the Affymetrix exon array data. The simplicity, flexibility and brevity of easyExon make it a valuable tool for AS event identification in genomic research.

UNLABELLED: ArrayFusion annotates conventional CGH results and various types of microarray data from a range of platforms (cDNA, expression, exon, SNP, array-CGH and ChIP-on-chip) and converts them into standard formats which can be visualized in genome browsers (Affymetrix Integrated Genome Browser and GBrowse in the HapMap Project). Converted files can then be imported simultaneously into a single genome browser to benefit a collective interpretation between different array results. ArrayFusion therefore provides a new type of tool facilitating the integration of CGH and array results to provide new experimental directions. AVAILABILITY: http://microarray.ym.edu.tw/tools/arrayfusion