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Temporal memory safety bugs, especially use-after-free and double free bugs, pose a major security threat to C programs. Real-world exploits utilizing these bugs enable attackers to read and write arbitrary memory locations, causing disastrous violations of confidentiality, integrity, and availability. Many previous solutions retrofit temporal memory safety to C, but they all either incur high performance overhead and/or miss detecting certain types of temporal memory safety bugs.

In this paper, we propose a temporal memory safety solution that is both efficient and comprehensive. Specifically, we extend Checked C, a spatially-safe extension to C, with temporally-safe pointers. These are implemented by combining two techniques: fat pointers and dynamic key-lock checks. We show that the fat-pointer solution significantly improves running time and memory overhead compared to the disjoint-metadata approach that provides the same level of protection. With empirical program data and hands-on experience porting real-world applications, we also show that our solution is practical in terms of backward compatibility---one of the major complaints about fat pointers.

 

Autophagy, as an intracellular degradation system, plays a critical role in plant immunity. However, the involvement of autophagy in the plant immune system and its function in plant nematode resistance are largely unknown. Here, we show that root-knot nematode (RKN;Meloidogyne incognita) infection induces autophagy in tomato (Solanum lycopersicum) and differentatgmutants exhibit high sensitivity to RKNs. The jasmonate (JA) signaling negative regulators JASMONATE-ASSOCIATED MYC2-LIKE 1 (JAM1), JAM2 and JAM3 interact with ATG8s via an ATG8-interacting motif (AIM), and JAM1 is degraded by autophagy during RKN infection. JAM1 impairs the formation of a transcriptional activation complex between ETHYLENE RESPONSE FACTOR 1 (ERF1) and MEDIATOR 25 (MED25) and interferes with transcriptional regulation of JA-mediated defense-related genes by ERF1. Furthermore, ERF1 acts in a positive feedback loop and regulates autophagy activity by transcriptionally activatingATGexpression in response to RKN infection. Therefore, autophagy promotes JA-mediated defense against RKNs via forming a positive feedback circuit in the degradation of JAMs and transcriptional activation by ERF1.

 

Abstract Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patients who suffered from the same cancer and received the same drug treatment, which we call cancer-drug groups. We then used the miRNA expression data in TCGA to evaluate each miRNA’s ability to predict the survival outcome of patients in each cancer-drug group. As a result, the identified miRNAs are predictive of survival outcomes in a cancer-specific and drug-specific manner. Notably, most of the drug-specific miRNA survival markers and their target genes showed consistency in terms of correlations in their expression and their correlations with survival. Some of the identified miRNAs were supported by published literature in contexts of various cancers. We explored several additional breast cancer datasets that provided miRNA expression and survival data, and showed that our drug-specific miRNA survival markers for breast cancer were able to effectively stratify the prognosis of patients in those additional datasets. Together, this analysis revealed drug-specific miRNA markers for cancer survival, which can be promising tools toward personalized medicine. 

This paper presents Kage: a system that protects the control data of both application and kernel code on microcontroller-based embedded systems. Kage consists of a Kage-compliant embedded OS that stores all control data in separate memory regions from untrusted data, a compiler that transforms code to protect these memory regions efficiently and to add forward-edge control-flow integrity checks, and a secure API that allows safe updates to the protected data. We implemented Kage as an extension to FreeRTOS, an embedded real-time operating system. We evaluated Kage’s performance using the CoreMark benchmark. Kage incurred a 5.2% average run-time overhead and 49.8% code size overhead. Furthermore, the code size overhead was only 14.2% when compared to baseline FreeRTOS with the MPU enabled. We also evaluated Kage’s security guarantees by measuring and analyzing reachable code-reuse gadgets. Compared to FreeRTOS, Kage reduces the number of reachable gadgets from 2,276 to 27, and the remaining 27 gadgets cannot be stitched together to launch a practical attack. 

This study reports a class of wireless, lightweight, and multifunctional chemical sensors for detection of biomarkers. 

Abstract The ability to predict the efficacy of cancer treatments is a longstanding goal of precision medicine that requires improved understanding of molecular interactions with drugs and the discovery of biomarkers of drug response. Identifying genes whose expression influences drug sensitivity can help address both of these needs, elucidating the molecular pathways involved in drug efficacy and providing potential ways to predict new patients’ response to available therapies. In this study, we integrated cancer type, drug treatment, and survival data with RNA-seq gene expression data from The Cancer Genome Atlas to identify genes and gene sets whose expression levels in patient tumor biopsies are associated with drug-specific patient survival using a log-rank test comparing survival of patients with low vs. high expression for each gene. This analysis was successful in identifying thousands of such gene–drug relationships across 20 drugs in 14 cancers, several of which have been previously implicated in the respective drug’s efficacy. We then clustered significant genes based on their expression patterns across patients and defined gene sets that are more robust predictors of patient outcome, many of which were significantly enriched for target genes of one or more transcription factors, indicating several upstream regulatory mechanisms that may be involved in drug efficacy. We identified a large number of genes and gene sets that were potentially useful as transcript-level biomarkers for predicting drug-specific patient survival outcome. Our gene sets were robust predictors of drug-specific survival and our results included both novel and previously reported findings, suggesting that the drug-specific survival marker genes reported herein warrant further investigation for insights into drug mechanisms and for validation as biomarkers to aid cancer therapy decisions.