Machine diagnosis based on artificial immune systems

Nowadays, many of the manufactory and industrial system has a diagnosis system on top of it, responsible for ensuring the lifetime of the system itself. It achieves this by performing both diagnosis and error recovery procedures in real production time, on each of the individual parts of the system. There are many paradigms currently being used for diagnosis. However, they still fail to answer all the requirements imposed by the enterprises making it necessary for a different approach to take place. This happens mostly on the error recovery paradigms since the great diversity that is nowadays present in the industrial environment makes it highly unlikely for every single error to be fixed under a real time, no production stop, perspective. This work proposes a still relatively unknown paradigm to manufactory. The Artificial Immune Systems (AIS), which relies on bio-inspired algorithms, comes as a valid alternative to the ones currently being used. The proposed work is a multi-agent architecture that establishes the Artificial Immune Systems, based on bio-inspired algorithms. The main goal of this architecture is to solve for a resolution to the error currently detected by the system. The proposed architecture was tested using two different simulation environment, each meant to prove different points of views, using different tests. These tests will determine if, as the research suggests, this paradigm is a promising alternative for the industrial environment. It will also define what should be done to improve the current architecture and if it should be applied in a decentralised system.

Induction of experimental mammary carcinogenesis in rats with 7,12-dimethylbenz(a)anthracene

PURPOSE: To test an experimental model of chemical mammary carcinogenesis induction in rats. METHODS: Twenty young virgin Sprague-Dawley female rats, aged 47 days, received 20 mg of 7,12-dimethylbenz(a)anthracene (DMBA) intragastrically by gavage. Afterwards, at 8 and 13 weeks, their mammary glands were examined. At the end of the experiment, the animals were sacrificed, and the mammary tumors were measured and weighed. Tumor fragments were analyzed using light microscopy. RESULTS: Eight weeks after DMBA injection, 16 rats presented at least 1 breast tumor (80%). After 13 weeks, all of them (100%) developed breast carcinomas that were confirmed by histopathological analysis. CONCLUSION: This experimental animal model of chemical mammary induced carcinogenesis is feasible and can be used in further experiments on the role of tumorigenic biomodulator substances.

Assessment of a novel Cu (II) complex as a potential anticancer agent

Widely used in cancer treatment, chemotherapy still faces hindering challenges, ranging from severe induced toxicity to drug resistance acquisition. As means to overcome these setbacks, newly synthetized compounds have recently come into play with the basis of improved pharmacokinetic/pharmacodynamic properties. With this mind-set, this project aimed towards the antiproliferative potential characterization of a group of metallic compounds. Additionally the incorporation of the compounds within a nanoformulation and within new combination strategies with commercial chemotherapeutic drugs was also envisaged. Cell viability assays presented copper (II) compound (K4) as the most promising, presenting an IC50 of 6.10 μM and 19.09 μM for HCT116 and A549 cell line respectively. Exposure in fibroblasts revealed a 9.18 μM IC50. Hoechst staining assays further revealed the compound’s predisposition to induce chromatin condensation and nuclear fragmentation in HCT116 upon exposure to K4 which was later demonstrated by flow cytometry and annexin V-FITC/propidium iodide double staining analysis (under 50 % cell death induction). The compound further revealed the ability to interact with major macromolecules such as DNA (Kb = 2.17x105 M-1), inducing structural brakes and retardation, and further affecting cell cycle progression revealing delay in S-phase. Moreover BSA interactions were also visible however not conclusive. Proteome profiling revealed overexpression of proteins involved in metabolic activity and underexpression of proteins involved in apoptosis thus corroborating Hoechst and apoptosis flow cytometry data. K4 nanoformulation suffered from several hindrances and was ill succeeded in part due to K4’s poor solubility in aqueous buffers. Other approaches were considered in this regard. Combined chemotherapy assays revealed high cytotoxicity for afatinib and lapatinib strategies. Lapatinib and K4 proteome profiling further revealed high apoptosis rates, high metabolic activity and activation of redundant proteins as part of compensatory mechanisms.

Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

The human eye as human-machine interface

Eye tracking as an interface to operate a computer is under research for a while and new systems are still being developed nowadays that provide some encouragement to those bound to illnesses that incapacitates them to use any other form of interaction with a computer. Although using computer vision processing and a camera, these systems are usually based on head mount technology being considered a contact type system. This paper describes the implementation of a human-computer interface based on a fully non-contact eye tracking vision system in order to allow people with tetraplegia to interface with a computer. As an assistive technology, a graphical user interface with special features was developed including a virtual keyboard to allow user communication, fast access to pre-stored phrases and multimedia and even internet browsing. This system was developed with the focus on low cost, user friendly functionality and user independency and autonomy.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Physiological responses of matrinxã (Brycon amazonicus) fed different levels of vitamin C and submitted to air exposure

The role of vitamin C on physiological responses of matrinxã (Brycon amazonicus) submitted to air exposure was analyzed. Nine hundred fish (70.15 g) were distributed in fifteen 500 l boxes (60 fish.box-1) and fed five rations (treatments): Control (no vitamin C); T100 (100 mg); T200 (200 mg); T400 (400 mg) and T800 (800 mg of vitamin C kg.ration-1). Each ration was offered to fish of three boxes during 60 days before the stress challenge that consisted of exposing fish to air for two minutes. Samplings were carried out for 5, 15, 30 and 60 minutes after the air exposure. Blood was collected for glucose, cortisol, total protein, sodium, chloride, hematocrit, hemoglobin determination, and white and red cell count. Liver was removed for hepatosomatic index (HSI) calculation and glycogen determination. Vitamin C did not affect the levels of cortisol, chloride, total protein, hemoglobin, leukocytes, hepatic glycogen or HSI in air exposed fish. Blood glucose levels elevation observed 60 minutes after the challenge did not depend on the levels of vitamin C, nor did the drop in serum sodium levels verified 60 minutes after stressor. In general, hematocrit did not change by effect of vitamin C but it was lower at 15 and 30 minutes after the challenge. The number of erythrocytes decreased in fish after 5 minute sampling in all treatments, especially at 30 and 60 minutes. The air exposure evoked alterations in stress indicators of matrinxã, and the vitamin C did not alter the responses.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Optimum dietary protein requirement for Amazonian Tambaqui, Colossoma macropomum Cuvier, 1818, fed fish meal free diets

Fish meal free diets were formulated to contain graded protein levels as 25% (diet 1), 30% (diet 2), 35% (diet 3) and 40% (diet 4). The diets were fed to tambaqui juveniles (Colossoma macropomum) (46.4 ± 6.3g) in randomly designed recirculating systems for 60 days, to determine the optimum protein requirement for the fish. The final weight of the fish, weight gain (28.1, 28.5, 32.2, 28.0g) and specific growth rate increased (P>0.05) consistently with increasing dietary protein up to treatment with 35% protein diet and then showed a declining trend. Feed intake followed the same trend resulting in best feed efficiency (62.5%) in fish fed diet with 35% protein. Similarly, the protein intake increased significantly with increasing dietary protein levels and reduced after the fish fed with 35% protein; while protein efficiency ratio (2.28, 1.99, 1.87, 1.74) decreased with increasing dietary protein levels. Carcass ash and protein had linear relationship with dietary protein levels while the lipid showed a decreasing trend. Ammonia content (0.68, 0.73, 0.81, 1.21 mg L-1) of the experimental waters also increased (P<0.05) with increasing protein levels while pH, dissolved oxygen and temperature remained fairly constant without any clear pattern of inclination. Broken-line estimation of the weight gain indicated 30% protein as the optimum requirement for the fish.

When pneumonia becomes a double congenital diaphragmatic hernia

When pneumonia becomes a double congenital diaphragmatic hernia

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Molecular regulation of flowering induction in Quercus suber

Dissertação de mestrado em Plant Molecular Biology, Biotechnology and Bioentrepeneurship

Hematological responses of tambaqui Colossoma macropomum (Serrassalmidae) fed with diets supplemented with essential oil from Mentha piperita (Lamiaceae) and challenged with Aeromonas hydrophila

ABSTRACTIn fish farmings, diseases can be reduced by using immunostimulants. The aim of this study was to evaluate the immunostimulant potential of Mentha piperita in tambaqui fed with 0, 0.5, 1.0 and 1.5% of oil per kg of commercial fish feed. The fish were inoculated with Aeromonas hydrophila to challenge them. Hematological and biochemical parameters were determined after 30 days of feeding and seven days after the challenge. There was no mortality and M. piperita oil did not influence fish production parameters. However, blood hemoglobin concentration (Hb) increased in the fish fed with 0.5 and 1.5% of oil per kg of diet; albumin increased in those fed with 1.0%; cholesterol increased in all groups with oil; and triglycerides increased in those fed with 0.5%. After the bacterial challenge, the fish showed decreases in Hb when fed with diet enriched with 1.5% oil per kg of diet, in mean corpuscular volume with 1.0% and in mean corpuscular hemoglobin concentration with 0 and 1.5%. Protein levels increased in groups with 0 and 1.5% of oil and albumin when fed with 0 and 1.0%; cholesterol levels increased in the control group; and high levels of triglycerides were observed in the groups with 0, 0.5 and 1.5%. Thus, M. piperita essential oil promoted hematological alterations in tambaqui and can be recommended in diets containing up to 1.0% per kg, because of the minimal physiological modifications caused. However, additional studies are necessary to obtain more information regarding to the physiological effects of this immunostimulant.

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores