Characterization of recombinant human lactoferrin N-glycans expressed in the milk of transgenic cows.

Lactoferrin (LF) is one of the most abundant bioactive glycoproteins in human milk. Glycans attached through N-glycosidic bonds may contribute to Lactoferrin functional activities. In contrast, LF is present in trace amounts in bovine milk. Efforts to increase LF concentration in bovine milk led to alternative approaches using transgenic cows to express human lactoferrin (hLF). This study investigated and compared N-glycans in recombinant human lactoferrin (rhLF), bovine lactoferrin (bLF) and human lactoferrin by Nano-LC-Chip-Q-TOF Mass Spectrometry. The results revealed a high diversity of N-glycan structures, including fucosylated and sialylated complex glycans that may contribute additional bioactivities. rhLF, bLF and hLF had 23, 27 and 18 N-glycans respectively with 8 N-glycan in common overall. rhLF shared 16 N-glycan with bLF and 9 N-glycan with hLF while bLF shared 10 N-glycan with hLF. Based on the relative abundances of N-glycan types, rhLF and hLF appeared to contain mostly neutral complex/hybrid N-glycans (81% and 52% of the total respectively) whereas bLF was characterized by high mannose glycans (65%). Interestingly, the majority of hLF N-glycans were fucosylated (88%), whereas bLF and rhLF had only 9% and 20% fucosylation, respectively. Overall, this study suggests that rhLF N-glycans share more similarities to bLF than hLF

Domestic chickens activate a piRNA defense against avian leukosis virus

PIWI-interacting RNAs (piRNAs) protect the germ line by targeting transposable elements (TEs) through the base-pair complementarity. We do not know how piRNAs co-evolve with TEs in chickens. Here we reported that all active TEs in the chicken germ line are targeted by piRNAs, and as TEs lose their activity, the corresponding piRNAs erode away. We observed de novo piRNA birth as host responds to a recent retroviral invasion. Avian leukosis virus (ALV) has endogenized prior to chicken domestication, remains infectious, and threatens poultry industry. Domestic fowl produce piRNAs targeting ALV from one ALV provirus that was known to render its host ALV resistant. This proviral locus does not produce piRNAs in undomesticated wild chickens. Our findings uncover rapid piRNA evolution reflecting contemporary TE activity, identify a new piRNA acquisition modality by activating a pre-existing genomic locus, and extend piRNA defense roles to include the period when endogenous retroviruses are still infectious. DOI: http://dx.doi.org/10.7554/eLife.24695.00

A Survey, Taxonomy, and Analysis of Network Security Visualization Techniques

Network security visualization is a relatively new field and is quickly gaining momentum. Network security visualization allows the display and projection of the network or system data, in hope to efficiently monitor and protect the system from any intrusions or possible attacks. Intrusions and attacks are constantly continuing to increase in number, size, and complexity. Textually reading through log files or other textual sources is currently insufficient to secure a network or system. Using graphical visualization, security information is presented visually, and not only by text. Without network security visualization, reading through log files or other textual sources is an endless and aggravating task for network security analysts. Visualization provides a method of displaying large volume of information in a relatively small space. It also makes patterns easier to detect, recognize, and analyze. This can help security experts to detect problems that may otherwise be missed in reading text based log files. Network security visualization has become an active research field in the past six years and a large number of visualization techniques have been proposed. A comprehensive analysis of the existing techniques is needed to help network security designers make informed decisions about the appropriate visualization techniques under various circumstances. Moreover, a taxonomy of the existing visualization techniques is needed to classify the existing network security visualization techniques and present a high level overview of the field. In this thesis, the author surveyed the field of network security visualization. Specifically, the author analyzed the network security visualization techniques from the perspective of data model, visual primitives, security analysis tasks, user interaction, and other design issues. Various statistics were generated from the literatures. Based on this analysis, the author has attempted to generate useful guidelines and principles for designing effective network security visualization techniques. The author also proposed a taxonomy for the security visualization techniques. To the author’s knowledge, this is the first attempt to generate a taxonomy for network security visualization. Finally, the author evaluated the existing network security visualization techniques and discussed their characteristics and limitations. For future research, the author also discussed some open research problems in this field. This research is a step towards a thorough analysis of the problem space and the solution space in network security visualization

Gene regulatory networks elucidating huanglongbing disease mechanisms.

Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas), especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein - protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes) would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation), sucrose metabolism (upregulation), and starch biosynthesis (upregulation). In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70) was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur

OccPoIs: Points of Interest based on Neural Network\u27s Key Recovery in Side-Channel Analysis through Occlusion

Deep neural networks (DNNs) represent a powerful technique for assessing cryptographic security concerning side-channel analysis (SCA) due to their ability to aggregate leakages automatically, rendering attacks more efficient without preprocessing. Nevertheless, despite their effectiveness, DNNs employed in SCA are predominantly black-box algorithms, posing considerable interpretability challenges. In this paper, we propose a novel technique called Key Guessing Occlusion (KGO) that acquires a minimal set of sample points required by the DNN for key recovery, which we call OccPoIs. These OccPoIs provide information on which areas of the traces are important to the DNN for retrieving the key, enabling evaluators to know where to refine their cryptographic implementation. After obtaining the OccPoIs, we first explore the leakages found in these OccPoIs to understand what the DNN is learning with first-order Correlation Power Analysis (CPA). We show that KGO obtains relevant sample points that have a high correlation with the given leakage model but also acquires sample points that first-order CPA fails to capture. Furthermore, unlike the first-order CPA in the masking setting, KGO obtains these OccPoIs without the knowledge of the shares or mask. Next, we employ the template attack (TA) using the OccPoIs to investigate if KGO could be used as a feature selection tool. We show that using the OccPoIs with TA can recover the key for all the considered synchronized datasets and is consistent as a feature selection tool even on datasets protected by first-order masking. Furthermore, it also allows a more efficient attack than other feature selections on the first-order masking dataset called ASCADf

Internal hacking detection using machine learning

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Being able to prevent and early detect insider threats through an automated forewarning system has been a massive challenge for large companies. In recent years, to fill this gap several anomaly user behavior algorithms based on machine learning have been proposed, experimentally evaluated and analyzed in numerous surveys. The present work was conducted in the cybersecurity department (DCY) of Altice Portugal (MEO) and aims to address this problem identifying the families of unsupervised anomaly detection techniques that are more effective for insider threats detection based on a large dataset corresponding to a collection of users’ access log records. To this end, multi-domain attributes related to possible insider threats are interactively extracted and processed, creating a summary of user account’s daily activity. A clusteringbased algorithm that groups and characterizes similar accounts was applied. Without any example anomalies required in the training set, anomaly detection techniques were computed over those profiles, identifying unusual changes in user account behavior on a current day. Finally, to make it easier for analysts and managers to understand the anomaly, anomaly metrics and a visualization dashboard were created. To evaluate the efficiency of this project ten insider threat scenarios were injected and was found that the system can successfully detect anomalous behavior that may be an insider threat event