'Cooperative Automated worm Response and Detection ImmuNe ALgorithm (CARDINAL) inspired by T-cell Immunity and Tolerance'

The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

The deterministic Dendritic Cell Algorithm

The Dendritic Cell Algorithm is an immune-inspired algorithm originally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to analyse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm.

Further exploration of the Dendritic Cell Algorithm: antigen multiplier and time windows

As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performance in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DCA, including antigen multiplier and moving time windows, are reported. The real-valued Negative Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision tree algorithm are used, to conduct a baseline comparison. The results suggest that the DCA is applicable to KDD 99 data set, and the antigen multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with contant-sized detectors is not applicable to the data set. And the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.

Exploration of the dendritic cell algorithm with the duration calculus

As one of the newest members in Articial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been applied to a range of problems. These applications mainly belong to the eld of anomaly detection. However, real-time detection, a new challenge to anomaly detection, requires improvement on the real-time capability of the DCA. To assess such capability, formal methods in the research of real-time systems can be employed. The ndings of the assessment can provide guideline for the future development of the algorithm. Therefore, in this paper we use an interval logic based method, named the Duration Calcu- lus (DC), to specify a simplied single-cell model of the DCA. Based on the DC specications with further induction, we nd that each individual cell in the DCA can perform its function as a detector in real-time. Since the DCA can be seen as many such cells operating in parallel, it is potentially capable of performing real-time detection. However, the analysis process of the standard DCA constricts its real-time capability. As a result, we conclude that the analysis process of the standard DCA should be replaced by a real-time analysis component, which can perform periodic analysis for the purpose of real-time detection.

Artificial immune systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.

Analysis of contributions of herpes simplex virus type 1 UL43 protein to induction of cell-cell fusion

Purpose: To investigate whether UL43 protein, which is highly conserved in alpha- and gamma herpes viruses, and a non-glycosylated transmembrane protein, is involved in virus entry and virus-induced cell fusion. Methods: Mutagenesis was accomplished by a markerless two-step Red recombination mutagenesis system implemented on the Herpes simplex virus 1 (HSV-1) bacterial artificial chromosome (BAC). Growth properties of HSV-1 UL43 mutants were analyzed using plaque morphology and one-step growth kinetics. SDS-PAGE and Western blot was employed to assay the synthesis of the viral glycoproteins. Virus-penetration was assayed to determine if UL43 protein is required for efficient virus entry. Results: Lack of UL43 expression resulted in significantly reduced plaque sizes of syncytial mutant viruses and inhibited cell fusion induced by gBΔ28 or gKsyn20 (p < 0.05). Deletion of UL43 did not affect overall expression levels of viral glycoproteins gB, gC, gD, and gH on HSV-1(F) BAC infected cell surfaces. Moreover, mutant viruses lacking UL43 gene exhibited slower kinetics of entry into Vero cells than the parental HSV-1(F) BAC. Conclusion: Thus, these results suggest an important role for UL43 protein in mediating virus-induced membrane fusion and efficient entry of virion into target cells.

tRNA anticodon loop modifications ensure protein homeostasis and cell morphogenesis in yeast

Using budding yeast, we investigated a negative interaction network among genes for tRNA modifications previously implicated in anticodon-codon interaction: 5-methoxy-carbonyl-methyl-2-thio-uridine (mcm5s2U34: ELP3, URM1), pseudouridine (Ψ38/39: DEG1) and cyclic N6-threonyl-carbamoyl-adenosine (ct6A37: TCD1). In line with functional cross talk between these modifications, we find that combined removal of either ct6A37 or Ψ38/39 and mcm5U34 or s2U34 results in morphologically altered cells with synthetic growth defects. Phenotypic suppression by tRNA overexpression suggests that these defects are caused by malfunction of tRNALysUUU or tRNAGlnUUG, respectively. Indeed, mRNA translation and synthesis of the Gln-rich prion Rnq1 are severely impaired in the absence of Ψ38/39 and mcm5U34 or s2U34, and this defect can be rescued by overexpression of tRNAGlnUUG. Surprisingly, we find that combined modification defects in the anticodon loops of different tRNAs induce similar cell polarity- and nuclear segregation defects that are accompanied by increased aggregation of cellular proteins. Since conditional expression of an artificial aggregation-prone protein triggered similar cytological aberrancies, protein aggregation is likely responsible for loss of morphogenesis and cytokinesis control in mutants with inappropriate tRNA anticodon loop modifications.

Organic photosensitizers and nanostructured semiconductors for photoredox catalysis and artificial photosynthesis

Over the course of evolution, Nature has elegantly learned to use light to drive chemical reactions. On the other hand, humans have only recently started learning how to play with this powerful tool to carry out chemical transformations. In particular, a step forward was possible thanks to molecules and materials that can absorb light and trigger a series of processes that can drive chemical reactions. However, scarce elements are extensively employed in the design of most of these compounds and considerations on their scarcity and toxicity have sparked interest on alternatives based on earth-abundant elements. In this framework, the focus of this thesis has been the development and employment of heavy-metal free chromophores and of earth-abundant oxides. The first chapter regards the functionalization of boron-dipyrromethenes (BODIPYs) so as to allow access to their triplet excited state and tune their redox potentials, which was achieved thanks to the design of orthogonal donor-acceptor dyads. The BODIPY dyads were used to promote a photoredox reaction, and the mechanism of the reaction was clarified. In the second chapter, organic chromophores that display thermally-activated delayed fluorescence (TADF) were studied. These were used to perform enantioselective photoredox reactions, and a mechanistic investigation allowed to elucidate the fate of these photosensitizers in the reaction. Thanks to their stronger reducing power, it was possible to demonstrate the employability of TADF dyes in artificial photosynthesis, as well. Last, the oxidation of biomass-derived compounds was studied in a photoelectrochemical cell. For this purpose, hematite photoanodes were synthesized in collaboration with Prof. Caramori’s group at the University of Ferrara (Italy) and they were tested in the presence of a redox mediator. In addition to this, the possibility of repurposing a copper(II) water oxidation catalyst for the oxidation of biomass was investigated in collaboration with Prof. Llobet’s group at ICIQ (Tarragona, Spain).

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.

Egfr Activating Mutations And Their Association With Response To Platinum-doublet Chemotherapy In Brazilian Non-small Cell Lung Cancer Patients.

The aim of the study was to analyze the frequency of epidermal growth factor receptor (EGFR) mutations in Brazilian non-small cell lung cancer patients and to correlate these mutations with response to benefit of platinum-based chemotherapy in non-small cell lung cancer (NSCLC). Our cohort consisted of prospective patients with NSCLCs who received chemotherapy (platinum derivates plus paclitaxel) at the [UNICAMP], Brazil. EGFR exons 18-21 were analyzed in tumor-derived DNA. Fifty patients were included in the study (25 with adenocarcinoma). EGFR mutations were identified in 6/50 (12 %) NSCLCs and in 6/25 (24 %) adenocarcinomas; representing the frequency of EGFR mutations in a mostly self-reported White (82.0 %) southeastern Brazilian population of NSCLCs. Patients with NSCLCs harboring EGFR exon 19 deletions or the exon 21 L858R mutation were found to have a higher chance of response to platinum-paclitaxel (OR 9.67 [95 % CI 1.03-90.41], p = 0.047). We report the frequency of EGFR activating mutations in a typical southeastern Brazilian population with NSCLC, which are similar to that of other countries with Western European ethnicity. EGFR mutations seem to be predictive of a response to platinum-paclitaxel, and additional studies are needed to confirm or refute this relationship.

Solid Lipid Nanoparticles For Hydrophilic Biotech Drugs: Optimization And Cell Viability Studies (caco-2 & Hepg-2 Cell Lines).

Insulin was used as model protein to developed innovative Solid Lipid Nanoparticles (SLNs) for the delivery of hydrophilic biotech drugs, with potential use in medicinal chemistry. SLNs were prepared by double emulsion with the purpose of promoting stability and enhancing the protein bioavailability. Softisan(®)100 was selected as solid lipid matrix. The surfactants (Tween(®)80, Span(®)80 and Lipoid(®)S75) and insulin were chosen applying a 2(2) factorial design with triplicate of central point, evaluating the influence of dependents variables as polydispersity index (PI), mean particle size (z-AVE), zeta potential (ZP) and encapsulation efficiency (EE) by factorial design using the ANOVA test. Therefore, thermodynamic stability, polymorphism and matrix crystallinity were checked by Differential Scanning Calorimetry (DSC) and Wide Angle X-ray Diffraction (WAXD), whereas the effect of toxicity of SLNs was check in HepG2 and Caco-2 cells. Results showed a mean particle size (z-AVE) width between 294.6 nm and 627.0 nm, a PI in the range of 0.425-0.750, ZP about -3 mV, and the EE between 38.39% and 81.20%. After tempering the bulk lipid (mimicking the end process of production), the lipid showed amorphous characteristics, with a melting point of ca. 30 °C. The toxicity of SLNs was evaluated in two distinct cell lines (HEPG-2 and Caco-2), showing to be dependent on the concentration of particles in HEPG-2 cells, while no toxicity in was reported in Caco-2 cells. SLNs were stable for 24 h in in vitro human serum albumin (HSA) solution. The resulting SLNs fabricated by double emulsion may provide a promising approach for administration of protein therapeutics and antigens.

Autologous Platelet Gel: Five Cases Illustrating Use On Sickle Cell Disease Ulcers.

Leg ulcers represent a particularly disabling complication in patients with sickle cell disease (SCD). Platelet gel (PG) is a novel therapeutic strategy used for accelerating wound healing of a wide range of tissues through the continuous release of platelet growth factors. Here, we describe the use of PG preparation according to Anitua's PRGF (preparations rich in growth factors) protocol for treating chronic nonhealing ulcers in patients with SCD. A positive response occurred in 3 patients with an area reduction of 85.7% to 100%, which occurred within 7 to 10 weeks, and a 35.2% and 20.5% of area reduction in 2 other patients, who however, had large ulcers. After calcium chloride addition, the platelet-rich plasmas demonstrated enhanced platelet-derived growth factors-BB (P < .001), transforming growth factor-β1 (P = .015), vascular endothelial growth factors (P = .03), and hepatocyte growth factors (nonsignificant) secretion. Furthermore, calcium chloride addition induced a significant decrease in platelet number (P = .0134) and there was no leukocyte detection in the PG product. These results demonstrate that PG treatment might impact the healing of leg ulcers in sickle cell disease, especially in patients with small ulcers.

Impaired Compensatory Beta-cell Function And Growth In Response To High-fat Diet In Ldl Receptor Knockout Mice.

In this study, we investigated the effect of low density lipoprotein receptor (LDLr) deficiency on gap junctional connexin 36 (Cx36) islet content and on the functional and growth response of pancreatic beta-cells in C57BL/6 mice fed a high-fat (HF) diet. After 60 days on regular or HF diet, the metabolic state and morphometric islet parameters of wild-type (WT) and LDLr-/- mice were assessed. HF diet-fed WT animals became obese and hypercholesterolaemic as well as hyperglycaemic, hyperinsulinaemic, glucose intolerant and insulin resistant, characterizing them as prediabetic. Also they showed a significant decrease in beta-cell secretory response to glucose. Overall, LDLr-/- mice displayed greater susceptibility to HF diet as judged by their marked cholesterolaemia, intolerance to glucose and pronounced decrease in glucose-stimulated insulin secretion. HF diet induced similarly in WT and LDLr-/- mice, a significant decrease in Cx36 beta-cell content as revealed by immunoblotting. Prediabetic WT mice displayed marked increase in beta-cell mass mainly due to beta-cell hypertrophy/replication. Nevertheless, HF diet-fed LDLr-/- mice showed no significant changes in beta-cell mass, but lower islet-duct association (neogenesis) and higher beta-cell apoptosis index were seen as compared to controls. The higher metabolic susceptibility to HF diet of LDLr-/- mice may be explained by a deficiency in insulin secretory response to glucose associated with lack of compensatory beta-cell expansion.