A known history of diabetes and ambient hyperglycaemia were independent predictors for death and morbidity in SARS patients. Metabolic control may improve the prognosis of SARS patients.

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graphmatching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.

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The task of epitope discovery and vaccine design is increasingly reliant on bioinformatics analytic tools and access to depositories of curated data relevant to immune reactions and specific pathogens. The Immune Epitope Database and Analysis Resource (IEDB) was indeed created to assist biomedical researchers in the development of new vaccines, diagnostics, and therapeutics. The Analysis Resource is freely available to all researchers and provides access to a variety of epitope analysis and prediction tools. The tools include validated and benchmarked methods to predict MHC class I and class II binding. The predictions from these tools can be combined with tools predicting antigen processing, TCR recognition, and B cell epitope prediction. In addition, the resource contains a variety of secondary analysis tools that allow the researcher to calculate epitope conservation, population coverage, and other relevant analytic variables. The researcher involved in vaccine design and epitope discovery will also be interested in accessing experimental published data, relevant to the specific indication of interest. The database component of the IEDB contains a vast amount of experimentally derived epitope data that can be queried through a flexible user interface. The IEDB is linked to other pathogen-specific and immunological database resources.

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Machine learning models, especially neural networks (NNs), have achieved outstanding performance on diverse and complex applications. However, recent work has found that they are vulnerable to Trojan attacks where an adversary trains a corrupted model with poisoned data or directly manipulates its parameters in a stealthy way. Such Trojaned models can obtain good performance on normal data during test time while predict incorrectly on the adversarially manipulated data samples. This paper aims to develop ways to detect Trojaned models. We mainly explore the idea of meta neural analysis, a technique involving training a meta NN model that can be used to predict whether or not a target NN model has certain properties. We develop a novel pipeline Meta Neural Trojaned model Detection (MNTD) system to predict if a given NN is Trojaned via meta neural analysis on a set of trained shadow models.We propose two ways to train the meta-classifier without knowing the Trojan attacker's strategies. The first one, oneclass learning, will fit a novel detection meta-classifier using only benign neural networks. The second one, called jumbo learning, will approximate a general distribution of Trojaned models and sample a "jumbo" set of Trojaned models to train the metaclassifier and evaluate on the unseen Trojan strategies. Extensive experiments demonstrate the effectiveness of MNTD in detecting different Trojan attacks in diverse areas such as vision, speech, tabular data, and natural language processing. We show that MNTD reaches an average of 97% detection AUC (Area Under the ROC Curve) score and outperforms existing approaches. Furthermore, we design and evaluate MNTD system to defend against strong adaptive attackers who have exactly the knowledge of the detection, which demonstrates the robustness of MNTD.

Introduction: The current outbreak of Zika virus has resulted in a massive effort to accelerate the development of ZIKV-specific diagnostics and vaccines. These efforts would benefit greatly from the definition of the specific epitope targets of immune responses in ZIKV, but given the relatively recent emergence of ZIKV as a pandemic threat, few such data are available.Methods: We used a large body of epitope data for other Flaviviruses that was available from the IEDB for a comparative analysis against the ZIKV proteome in order to project targets of immune responses in ZIKV.Results: We found a significant level of overlap between known antigenic sites from other Flavivirus proteins with residues on the ZIKV polyprotein. The E and NS1 proteins shared functional antibody epitope sites, whereas regions of T cell reactivity were conserved within NS3 and NS5 for ZIKV. Discussion: Our epitope based analysis provides guidance for which regions of the ZIKV polyprotein are most likely unique targets of ZIKV-specific antibodies, and which targets in ZIKV are most likely to be cross-reactive with other Flavivirus species. These data may therefore provide insights for the development of antibody- and T cell-based ZIKV-specific diagnostics, therapeutics and prophylaxis.

SUMMARY Proliferating cell nuclear antigen (PCNA) is a pivotal replication protein, which also controls cellular responses to DNA damage. Posttranslational modification of PCNA by SUMO and ubiquitin modulate these responses. How the modifiers alter PCNA-dependent DNA repair and damage tolerance pathways is largely unknown. We used hybrid methods to identify atomic models of PCNAK107-Ub and PCNAK164-SUMO consistent with small angle X-ray scattering (SAXS) data of these complexes in solution. We show that SUMO and ubiquitin have distinct modes of association to PCNA. Ubiquitin adopts discrete docked binding positions. By contrast, SUMO associates by simple tethering and adopts extended flexible conformations. These structural differences are the result of the opposite electrostatic potentials of SUMO and Ub. The unexpected contrast in conformational behavior of Ub-PCNA and SUMO-PCNA has implications for interactions with partner proteins, interacting surfaces accessibility, and access points for pathway regulation.