The application of a novel, cell permeable activity-based probe for the detection of cysteine cathepsins.

Cysteine cathepsins, such as cathepsin S (CTSS), are implicated in the pathology of a wide range of diseases and are of potential utility as diagnostic and prognostic biomarkers. In previous work, we demonstrated the potency and efficiency of a biotinylated diazomethylketone (DMK)-based activity-based probe (ABP), biotin-PEG-LVG-DMK, for disclosure of recombinant CTSS and CTSS in cell lysates. However, the limited cell permeability of both the biotin and spacer groups restricted detection of CTSS to cell lysates. The synthesis and characterisation of a cell permeable ABP to report on intracellular CTSS activity is reported. The ABP, Z-PraVG-DMK, a modified peptidyl diazomethylketone, was based on the N-terminus of human cystatin motif (Leu-Val-Gly). The leucine residue was substituted for the alkyne-bearing proparcylglycine to facilitate conjugation of an azide-tagged reporter group using click chemistry, following irreversible inhibition of CTSS. When incubated with viable Human Embryonic Kidney 293 cells, Z-PraVG-DMK permitted disclosure of CTSS activity following cell lysis and rhodamine azide conjugation, by employing standard click chemistry protocols. Furthermore, the fluorescent tag facilitated direct detection of CTSS using in-gel fluorescent scanning, obviating the necessity for downstream biotin-streptavidin conjugation and detection procedures.

The ENCARA system for face detection and normalization

[EN]Facial image processing is becoming widespread in human-computer applications, despite its complexity. High-level processes such as face recognition or gender determination rely on low-level routines that must e ectively detect and normalize the faces that appear in the input image. In this paper, a face detection and normalization system is described. The approach taken is based on a cascade of fast, weak classi ers that together try to determine whether a frontal face is present in the image.

Cyber Enigmas? Passive Detection and Pedagogical Agents: Can Students Spot the fake?

This paper presents a study that was undertaken to examine human interaction with a pedagogical agent and the passive and active detection of such agents within a synchronous, online environment. A pedagogical agent is a software application which can provide a human like interaction using a natural language interface. These may be familiar from the smartphone interfaces such as ‘Siri’ or ‘Cortana’, or the virtual online assistants found on some websites, such as ‘Anna’ on the Ikea website. Pedagogical agents are characters on the computer screen with embodied life-like behaviours such as speech, emotions, locomotion, gestures, and movements of the head, the eye, or other parts of the body. The passive detection test is where participants are not primed to the potential presence of a pedagogical agent within the online environment. The active detection test is where participants are primed to the potential presence of a pedagogical agent. The purpose of the study was to examine how people passively detected pedagogical agents that were presenting themselves as humans in an online environment. In order to locate the pedagogical agent in a realistic higher education online environment, problem-based learning online was used. Problem-based learning online provides a focus for discussions and participation, without creating too much artificiality. The findings indicated that the ways in which students positioned the agent tended to influence the interaction between them. One of the key findings was that since the agent was focussed mainly on the pedagogical task this may have hampered interaction with the students, however some of its non-task dialogue did improve students' perceptions of the autonomous agents’ ability to interact with them. It is suggested that future studies explore the differences between the relationships and interactions of learner and pedagogical agent within authentic situations, in order to understand if students' interactions are different between real and virtual mentors in an online setting.

Optimization of immunofluorescence protocols for detection of biomarkers in colorectal and breast cancer tissues

Cancer remains an undetermined question for modern medicine. Every year millions of people ranging from children to adult die since the modern treatment is unable to meet the challenge. Research must continue in the area of new biomarkers for tumors. Molecular biology has evolved during last years; however, this knowledge has not been applied into the medicine. Biological findings should be used to improve diagnostics and treatment modalities. In this thesis, human formalin-fixed paraffin embedded colorectal and breast cancer samples were used to optimize the double immunofluorescence staining protocol. Also, immunohistochemistry was performed in order to visualize expression patterns of each biomarker. Concerning double immunofluorescence, feasibility of primary antibodies raised in different and same host species was also tested. Finally, established methods for simultaneous multicolor immunofluorescence imaging of formalin-fixed paraffin embedded specimens were applied for the detection of pairs of potential biomarkers of colorectal cancer (EGFR, pmTOR, pAKT, Vimentin, Cytokeratin Pan, Ezrin, E-cadherin) and breast cancer (Securin, PTTG1IP, Cleaved caspase 3, ki67).

Detecting Danger: Applying a Novel Immunological Concept to Intrusion Detection Systems'

INTRODUCTION In recent years computer systems have become increasingly complex and consequently the challenge of protecting these systems has become increasingly difficult. Various techniques have been implemented to counteract the misuse of computer systems in the form of firewalls, antivirus software and intrusion detection systems. The complexity of networks and dynamic nature of computer systems leaves current methods with significant room for improvement. Computer scientists have recently drawn inspiration from mechanisms found in biological systems and, in the context of computer security, have focused on the human immune system (HIS). The human immune system provides an example of a robust, distributed system that provides a high level of protection from constant attacks. By examining the precise mechanisms of the human immune system, it is hoped the paradigm will improve the performance of real intrusion detection systems. This paper presents an introduction to recent developments in the field of immunology. It discusses the incorporation of a novel immunological paradigm, Danger Theory, and how this concept is inspiring artificial immune systems (AIS). Applications within the context of computer security are outlined drawing direct reference to the underlying principles of Danger Theory and finally, the current state of intrusion detection systems is discussed and improvements suggested.

'Immune System Approaches to Intrusion Detection - A Review'

Abstract. The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.

'Malicious Code Execution Detection and Response Immune System inspired by the Danger Theory'

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.

Specific serology for emerging human coronaviruses by protein microarray

We present a serological assay for the specific detection of IgM and IgG antibodies against the emerging human coronavirus hCoV-EMC and the SARS-CoV based on protein microarray technology. The assay uses the S1 receptor-binding subunit of the spike protein of hCoV-EMC and SARS-CoV as antigens. The assay has been validated extensively using putative cross-reacting sera of patient cohorts exposed to the four common hCoVs and sera from convalescent patients infected with hCoV-EMC or SARS-CoV.

Immune System Approaches to Intrusion Detection - A Review

The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we review the algorithms used, the development of the systems and the outcome of their implementation. We provide an introduction and analysis of the key developments within this field, in addition to making suggestions for future research.

Integrating Innate and Adaptive Immunity for Intrusion Detection

Network Intrusion Detection Systems (NIDS) monitor a net- work with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDS’s rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alerts, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to an intrusion detection problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.

Dendritic Cells for SYN Scan Detection

Artificial immune systems have previously been applied to the problem of intrusion detection. The aim of this research is to develop an intrusion detection system based on the function of Dendritic Cells (DCs). DCs are antigen presenting cells and key to the activation of the human immune system, behaviour which has been abstracted to form the Dendritic Cell Algorithm (DCA). In algorithmic terms, individual DCs perform multi-sensor data fusion, asynchronously correlating the fused data signals with a secondary data stream. Aggregate output of a population of cells is analysed and forms the basis of an anomaly detection system. In this paper the DCA is applied to the detection of outgoing port scans using TCP SYN packets. Results show that detection can be achieved with the DCA, yet some false positives can be encountered when simultaneously scanning and using other network services. Suggestions are made for using adaptive signals to alleviate this uncovered problem.

Dendritic Cells for Real-Time Anomaly Detection

Dendritic Cells (DCs) are innate immune system cells which have the power to activate or suppress the immune system. The behaviour of human DCs is abstracted to form an algorithm suitable for anomaly detection. We test this algorithm on the real-time problem of port scan detection. Our results show a significant difference in artificial DC behaviour for an outgoing portscan when compared to behaviour for normal processes.

TRIB2 in human AML: a biological and clinical investigation

Acute myeloid leukemia (AML) involves the proliferation, abnormal survival and arrest of cells at a very early stage of myeloid cell differentiation. The biological and clinical heterogeneity of this disease complicates treatment and highlights the significance of understanding the underlying causes of AML, which may constitute potential therapeutic targets, as well as offer prognostic information. Tribbles homolog 2 (Trib2) is a potent murine oncogene capable of inducing transplantable AML with complete penetrance. The pathogenicity of Trib2 is attributed to its ability to induce proteasomal degradation of the full length isoform of the transcription factor CCAAT/enhancer-binding protein alpha (C/EBPα p42). The role of TRIB2 in human AML cells, however, has not been systematically investigated or targeted. Across human cancers, TRIB2 oncogenic activity was found to be associated with its elevated expression. In the context of AML, TRIB2 overexpression was suggested to be associated with the large and heterogeneous subset of cytogenetically normal AML patients. Based upon the observation that overexpression of TRIB2 has a role in cellular transformation, the effect of modulating its expression in human AML was examined in a human AML cell line that expresses high levels of TRIB2, U937 cells. Specific suppression of TRIB2 led to impaired cell growth, as a consequence of both an increase in apoptosis and a decrease in cell proliferation. Consistent with these in vitro results, TRIB2 silencing strongly reduced progression of the U937 in vivo xenografts, accompanied by detection of a lower spleen weight when compared with mice transplanted with TRIB2- expressing control cells. Gene expression analysis suggested that TRIB2 modulates apoptosis and cell-cycle sensitivity by influencing the expression of a subset of genes known to have implications on these phenotypes. Furthermore, TRIB2 was found to be expressed in a significant subset of AML patient samples analysed. To investigate whether increased expression of this gene could be afforded prognostic significance, primary AML cells with dichotomized levels of TRIB2 transcripts were evaluated in terms of their xenoengraftment potential, an assay reported to correlate with disease aggressiveness observed in humans. A small cohort of analysed samples with higher TRIB2 expression did not associate with preferential leukaemic cell engraftment in highly immune-deficient mice, hence, not predicting for an adverse prognosis. However, further experiments including a larger cohort of well characterized AML patients would be needed to clarify TRIB2 significance in the diagnostic setting. Collectively, these data support a functional role for TRIB2 in the maintenance of the oncogenic properties of human AML cells and suggest TRIB2 can be considered a rational therapeutic target. Proteasome inhibition has emerged as an attractive target for the development of novel anti-cancer therapies and results from translational research and clinical trials support the idea that proteasome inhibitors should be considered in the treatment of AML. The present study argued that proteasome inhibition would effectively inhibit the function of TRIB2 by abrogating C/EBPα p42 protein degradation and that it would be an effective pharmacological targeting strategy in TRIB2-positive AMLs. Here, a number of cell models expressing high levels of TRIB2 were successfully targeted by treatment with proteasome inhibitors, as demonstrated by multiple measurements that included increased cytotoxicity, inhibition of clonogenic growth and anti-AML activity in vivo. Mechanistically, it was shown that block of the TRIB2 degradative function led to an increase of C/EBPα p42 and that response was specific to the TRIB2-C/EBPα axis. Specificity was addressed by a panel of experiments showing that U937 cells (express detectable levels of endogenous TRIB2 and C/EBPα) treated with the proteasome inhibitor bortezomib (Brtz) displayed a higher cytotoxic response upon TRIB2 overexpression and that ectopic expression of C/EBPα rescued cell death. Additionally, in C/EBPα-negative leukaemia cells, K562 and Kasumi 1, Brtz-induced toxicity was not increased following TRIB2 overexpression supporting the specificity of the compound on the TRIB2-C/EBPα axis. Together these findings provide pre-clinical evidence that TRIB2- expressing AML cells can be pharmacologically targeted with proteasome inhibition due, in part, to blockage of the TRIB2 proteolytic function on C/EBPα p42. A large body of evidence indicates that AML arises through the stepwise acquisition of genetic and epigenetic changes. Mass spectrometry data has identified an interaction between TRIB2 and the epigenetic regulator Protein Arginine Methyltransferase 5 (PRMT5). Following assessment of TRIB2‟s role in AML cell survival and effective targeting of the TRIB2-C/EBPα degradation pathway, a putative TRIB2/PRMT5 cooperation was investigated in order to gain a deeper understanding of the molecular network in which TRIB2 acts as a potent myeloid oncogene. First, a microarray data set was interrogated for PRMT5 expression levels and the primary enzyme responsible for symmetric dimethylation was found to be transcribed at significantly higher levels in AML patients when compared to healthy controls. Next, depletion of PRMT5 in the U937 cell line was shown to reduce the transformative phenotype in the high expressing TRIB2 AML cells, which suggests that PRMT5 and TRIB2 may cooperate to maintain the leukaemogenic potential. Importantly, PRMT5 was identified as a TRIB2-interacting protein by means of a protein tagging approach to purify TRIB2 complexes from 293T cells. These findings trigger further research aimed at understanding the underlying mechanism and the functional significance of this interplay. In summary, the present study provides experimental evidence that TRIB2 has an important oncogenic role in human AML maintenance and, importantly in such a molecularly heterogeneous disease, provides the rational basis to consider proteasome inhibition as an effective targeting strategy for AML patients with high TRIB2 expression. Finally, the identification of PRMT5 as a TRIB2-interacting protein opens a new level of regulation to consider in AML. This work may contribute to our further understanding and therapeutic strategies in acute leukaemias.

Dendritic Cells for Anomaly Detection

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

An Immune Network Intrusion Detection System Utilising Correlation Context

Network Intrusion Detection Systems (NIDS) are computer systems which monitor a network with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDSs rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alerts, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to the IDS problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.