ABSTRACT:The relationship between aphids and their host plants is thought to be functionally analogous to plant-pathogen interactions. Although virulence effector proteins that mediate plant defenses are well-characterized for pathogens such as bacteria, oomycetes, and nematodes, equivalent molecules in aphids and other phloem-feeders are poorly understood. A dual transcriptomic-proteomic approach was adopted to generate a catalog of candidate effector proteins from the salivary glands of the pea aphid, Acyrthosiphon pisum. Of the 1557 transcript supported and 925 mass spectrometry identified proteins, over 300 proteins were identified with secretion signals, including proteins that had previously been identified directly from the secreted saliva. Almost half of the identified proteins have no homologue outside aphids and are of unknown function. Many of the genes encoding the putative effector proteins appear to be evolving at a faster rate than homologues in other insects, and there is strong evidence that genes with multiple copies in the genome are under positive selection. Many of the candidate aphid effector proteins were previously characterized in typical phytopathogenic organisms (e.g., nematodes and fungi) and our results highlight remarkable similarities in the saliva from plant-feeding nematodes and aphids that may indicate the evolution of common solutions to the plant-parasitic lifestyle.

Gall-forming arthropods are highly specialized herbivores that, in combination with their hosts, produce extended phenotypes with unique morphologies [1]. Many are economically important, and others have improved our understanding of ecology and adaptive radiation [2]. However, the mechanisms that these arthropods use to induce plant galls are poorly understood. We sequenced the genome of the Hessian fly (Mayetiola destructor; Diptera: Cecidomyiidae), a plant parasitic gall midge and a pest of wheat (Triticum spp.), with the aim of identifying genic modifications that contribute to its plant-parasitic lifestyle. Among several adaptive modifications, we discovered an expansive reservoir of potential effector proteins. Nearly 5% of the 20,163 predicted gene models matched putative effector gene transcripts present in the M. destructor larval salivary gland. Another 466 putative effectors were discovered among the genes that have no sequence similarities in other organisms. The largest known arthropod gene family (family SSGP-71) was also discovered within the effector reservoir. SSGP-71 proteins lack sequence homologies to other proteins, but their structures resemble both ubiquitin E3 ligases in plants and E3-ligase-mimicking effectors in plant pathogenic bacteria. SSGP-71 proteins and wheat Skp proteins interact in vivo. Mutations in different SSGP-71 genes avoid the effector-triggered immunity that is directed by the wheat resistance genes H6 and H9. Results point to effectors as the agents responsible for arthropod-induced plant gall formation.

BeetleBase (http://www.beetlebase.org) has been updated to provide more comprehensive genomic information for the red flour beetle Tribolium castaneum. The database contains genomic sequence scaffolds mapped to 10 linkage groups (genome assembly release Tcas_3.0), genetic linkage maps, the official gene set, Reference Sequences from NCBI (RefSeq), predicted gene models, ESTs and whole-genome tiling array data representing several developmental stages. The database was reconstructed using the upgraded Generic Model Organism Database (GMOD) modules. The genomic data is stored in a PostgreSQL relatational database using the Chado schema and visualized as tracks in GBrowse. The updated genetic map is visualized using the comparative genetic map viewer CMAP. To enhance the database search capabilities, the BLAST and BLAT search tools have been integrated with the GMOD tools. BeetleBase serves as a long-term repository for Tribolium genomic data, and is compatible with other model organism databases.

This paper motivates and precisely formulates the problem of learning from distributed data; describes a general strategy for transforming traditional machine learning algorithms into algorithms for learning from distributed data; demonstrates the application of this strategy to devise algorithms for decision tree induction from distributed data; and identifies the conditions under which the algorithms in the distributed setting are superior to their centralized counterparts in terms of time and communication complexity; The resulting algorithms are provably exact in that the decision tree constructed from distributed data is identical to that obtained in the centralized setting. Some natural extensions leading to algorithms for learning from heterogeneous distributed data and learning under privacy constraints are outlined.

Although Machine Learning (ML) based approaches have shown promise for Android malware detection, a set of critical challenges remain unaddressed. Some of those challenges arise in relation to proper evaluation of the detection approach while others are related to the design decisions of the same. In this paper, we systematically study the impact of these challenges as a set of research questions (i.e., hypotheses). We design an experimentation framework where we can reliably vary several parameters while evaluating ML-based Android malware detection approaches. The results from the experiments are then used to answer the research questions. Meanwhile, we also demonstrate the impact of some challenges on some existing ML-based approaches. The large (market-scale) dataset (benign and malicious apps) we use in the above experiments represents the real-world Android app security analysis scale. We envision this study to encourage the practice of employing a better evaluation strategy and better designs of future ML-based approaches for Android malware detection.

No abstract

Social media platforms such as Twitter provide valuable information for aiding disaster response during emergency events. Machine learning could be used to identify such information. However, supervised learning algorithms rely on labelled data, which is not readily available for an emerging target disaster. While labelled data might be available for a prior source disaster, supervised classifiers learned only from the source disaster may not perform well on the target disaster, as each event has unique characteristics (e.g., type, location, and culture) and may cause different social media responses. To address this limitation, we propose to use a domain adaptation approach, which learns classifiers from unlabelled target data, in addition to source labelled data. Our approach uses the Naïve Bayes classifier, together with an iterative Self‐Training strategy. Experimental results on the task of identifying tweets relevant to a disaster of interest show that the domain adaptation classifiers are better as compared to the supervised classifiers learned only from labelled source data.

Software vulnerabilities represent a major cause of cybersecurity problems. The National Vulnerability Database (NVD) is a public data source that maintains standardized information about reported software vulnerabilities. Since its inception in 1997, NVD has published information about more than 43,000 software vulnerabilities affecting more than 17,000 software applications. This information is potentially valuable in understanding trends and patterns in software vulnerabilities, so that one can better manage the security of computer systems that are pestered by the ubiquitous software security flaws. In particular, one would like to be able to predict the likelihood that a piece of software contains a yet-to-be-discovered vulnerability, which must be taken into account in security management due to the increasing trend in zero-day attacks. We conducted an empirical study on applying data-mining techniques on NVD data with the objective of predicting the time to next vulnerability for a given software application. We experimented with various features constructed using the information available in NVD, and applied various machine learning algorithms to examine the predictive power of the data. Our results show that the data in NVD generally have poor prediction capability, with the exception of a few vendors and software applications. By doing a large number of experiments and observing the data, we suggest several reasons for why the NVD data have not produced a reasonable prediction model for time to next vulnerability with our current approach.