Search results for: Marla Trindade

Authors: Ridwaan N. Milase, Leonardo J. Van Zyl, Marla Trindade

Abstract:

American foulbrood (AFB) is the world’s most devastating honeybee disease that has drastically reduced the population of Apis mellifera capensis since 2009. The outbreak has jeopardized the South African bee keeping industry as well as the agricultural sector dependent on honeybees for honey production and pollination, leading to significant economic losses. AFB is caused by Paenibacillus larvae, a spore-forming, Gram positive facultative anaerobic and flagellated bacterium. The use of antibiotics within beehives has selected for resistant strains of P. larvae, while the current practice of burning spore contaminated beehives and equipment contributes to the economic losses in the honeybee-keeping industry. Therefore, phage therapy is proposed as a promising alternative to combat P. larvae strains affecting A. mellifera capensis. The genomes of two P. larvae strains isolated from infected combs in the Western Cape have been sequenced and annotated using bioinformatics tools. Genome analyses has revealed that these P. larvae strains are lysogens to more than 6 different prophages and possess different type of clustered regularly interspaced short palindromic repeat (CRISPRs) regions per strain. Active prophages from one of the two P. larvae strains were detected and identified using PCR. Electron microscopy was used to determine the family of the identified active prophages. Lytic bacteriophages that specifically target the two P. larvae strains were purified from sewage wastewater, beehive materials, and soil samples to investigate their potential development as anti-P. larvae agents. Another alternative treatment being investigated is the development of a prophage endolysin cocktail. Endolysin genes of the prophages have been targeted, cloned and expressed in Escherichia coli. The heterologously expressed endolysins have been purified and are currently being assessed for their lytic activity against P. larvae strains and other commensal microorganisms that compose the honeybee larvae microbiota. The study has shown that phage therapy and endolysins have a great potential as alternative control methods for AFB disease affecting A. mellifera capensis.

Keywords: American foulbrood, bacteriophage, honeybee, Paenibacillus larvae

Procedia PDF Downloads 151

Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade

Abstract:

Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

Procedia PDF Downloads 355

Authors: D. S. V. Barbieri, R. R. Gomes, G. D. Santos, P. F. Herkert, M. Moreira, E. S. Trindade, V. A. Vicente

Abstract:

The importance of yeast infections has increased in recent decades. The monitoring of Candida yeasts has been relevant in the study of groups and populations. This research evaluated 31 Candida spp. isolates from oral microbiota of 12 Brazilian schoolchildren coinfected with Streptococcus mutans. The isolates were evaluated for their ability to form biofilm in vitro and molecularly characterized based on the sequencing of intergenic spacer regions ITS1-5,8S-ITS2 and variable domains of the large subunit (D1/D2) regions of the rDNA, as well as ABC system genotyping. The sequencing confirmed 26 lineages of Candida albicans, three Candida tropicalis, one Candida guillhermondii and one Candida glabrata. Genetic variability and differences on in biofilm formation were observed among Candida yeasts lineages. At least one Candida strain from each caries activity child was C.albicans genotype A or Candida non-albicans. C. tropicalis was associated with highest cavities rates. These results indicate that the presence of C. albicans genotype A or multi-colonization by non albicans species seem to be associates to the potentialization of caries risk.

Keywords: biofilm, Candida albicans, oral microbiota, caries

Procedia PDF Downloads 483

Authors: Amanda de Oliveira Ferreira Leite, Ana Luiza del Pino Ferreira, Bruna Garcez Correa, Janaíne de Souza Mello, Marla Manquevich, Mirna Wetters Portuguez

Abstract:

Objective: We sought to investigate a neuropsychological intervention focused on improving cognition, psychological aspects, and quality of life of elderly people with mild cognitive impairment. Method: A controlled and randomized study, blind to the evaluator, was executed. We evaluated 78 elderly people, divided into the neuropsychological and control groups, through a semi-structured interview, Addenbrooke’s Cognitive Examination, Katz Index, Lawton and Brody Scale, Geriatric Depression Scale, Beck Anxiety Inventory, Personal Development Scale, WHOQOL-bref and WHOQOL--old. Results: After the intervention, the neuropsychological group showed improvement in the cognitive subtests and in the total score, reduction in the frequency of symptoms associated with anxiety and depression, better psychological well-being, and quality of life. The research highlights useful intervention strategies for improving the general condition of these patients and rehabilitating damaged areas. Conclusion: We concluded that there is a relationship between neuropsychological intervention and improvement in cognitive and psychological performance, as well as in the quality of life in elderly people with mild cognitive impairment.

Keywords: aging, mild cognitive impairment, neuropsychology, quality of life

Procedia PDF Downloads 75

Authors: Michael Ricardo Lang, AdriéLle Costa, Ivana Iesbik, Karine Haag, Leonardo Trindade Buffara, Oscar Reimann Junior, Chelin Auswaldt Steclan

Abstract:

Stroke affects not only the individual, but has significant impacts on the social and family context. Therefore, it is necessary to know the peculiarities of each region, in order to contribute to regional public health policies effectively. Thus, the present study discusses biomarkers in a post-stroke population, admitted to a stroke unit (U-stroke) of reference in the southern region of Brazil. Biomarkers were analyzed, such as age, length of stay, mortality rate, survival time, risk factors and family history of stroke in patients after ischemic stroke. In this studied population, comparing men and women, it was identified that men were more affected than women, and the average age of women affected was higher, as they also had the highest mortality rate and the shortest hospital stay. The risk factors identified here were according to the global scenario; with SAH being the most frequent and those associated with sedentary lifestyle in women the most frequent (dyspilipidemia, heart disease and obesity). In view of this, the importance of studies that characterize populations regionally is evident, strengthening the strategic planning of policies in favor of health care.

Keywords: biomarkers, sex, stroke, stroke unit, population

Procedia PDF Downloads 230

Authors: Luiz Daniel Garay Trindade, Antonio De Freitas Valle Neto, Fabio Paulo Basso, Elder De Macedo Rodrigues, Maicon Bernardino, Daniel Welfer, Daniel Muller

Abstract:

Agriculture plays an important role in society since it is one of the main sources of food in the world. To help the production and yield of crops, precision agriculture makes use of technologies aiming at improving productivity and quality of agricultural commodities. One of the problems hampering quality of agricultural production is the disease affecting crops. Failure in detecting diseases in a short period of time can result in small or big damages to production, causing financial losses to farmers. In order to provide a map of the contributions destined to the early detection of plant diseases and a comparison of the accuracy of the selected studies, a systematic literature review of the literature was performed, showing techniques for digital image processing and neural networks. We found 35 interesting tool support alternatives to detect disease in 19 plants. Our comparison of these studies resulted in an overall average accuracy of 87.45%, with two studies very closer to obtain 100%.

Keywords: pattern recognition, image processing, deep learning, precision agriculture, smart farming, agricultural automation

Procedia PDF Downloads 342

Authors: Marla Wendy Spergel

Abstract:

The purpose of this qualitative study is to showcase graduated high school students’ to voice on the impact past teachers had on their motivation to learn, and if this impact has affected their post-high-school lives. Through a focus group strategy, 21 graduated high school alumni participated in three separate focus groups. Participants discussed their former teacher’s emotional intelligence skills, which influenced their motivation to learn or not. A focused review of the literature revealed that teachers are a major factor in a student’s motivation to learn. This research was guided by Bandura’s Social Cognitive Theory of Motivation and constructs related to learning and motivation from Carl Rogers’ Humanistic Views of Personality, and from Brain-Based Learning perspectives with a major focus on the area of Emotional Intelligence. Findings revealed that the majority of participants identified teachers who most motivated them to learn and demonstrated skills associated with emotional intelligence. An important and disturbing finding relates to the saliency of negative experiences. Further work is recommended to expand this line of study in Higher Education, perform a long-term study to better gain insight into long-term benefits attributable to experiencing positive teachers, study the negative impact teachers have on students’ motivation to learn, specifically focusing on student anxiety and acquired helplessness.

Keywords: emotional intelligence, learning, motivation, pedagogy

Procedia PDF Downloads 130

Authors: Alice D. Dioquino, Olivia N. Buzon, Emilio F. Aguinaldo, Ruel Avila, Erwin R. Callo, Cristy Ocampo, Malvin R. Tabajen, Marla C. Papango, Marilou M. Ubina, Josephine Tondo, Cromwell L. Valeriano

Abstract:

The main purpose of this study was to develop, evaluate and validate courseware on Selected Topics in Mathematics, Science, and the English Language. Specifically, it aimed to: 1. Identify the appropriate Instructional Systems Design (ISD) model in the development of the courseware material; 2. Assess the courseware material according to its: a. Content Characteristics; b. Instructional Characteristics; and c. Technical Characteristics 3. Find out if there is a significant difference in the performance of students before and after using the tutorial CAI. This research is developmental as well as a one group pretest-posttest design. The study had two phases. Phase I includes the needs analysis, writing of lessons and storyboard by the respective experts in each field. Phase II includes the digitization or the actual development of the courseware by the faculty of the ICT department. In this phase it adapted an instructional systems design (ISD) model which is the ADDIE model. ADDIE stands for Analysis, Design, Development, Implementation and Evaluation. Formative evaluation was conducted simultaneously with the different phases to detect and remedy any bugs in the courseware along the areas of content, instructional and technical characteristics. The expected output are the digitized lessons in Algebra, Biology, Chemistry, Physics and Communication Arts in English. Students and some IT experts validated the CAI material using the Evaluation Form by Wong & Wong. They validated the CAI materials as Highly Acceptable with an overall mean rating of 4.527and standard deviation of 0 which means that they were one in the ratings they have given the CAI materials. A mean gain was recorded and computing the t-test for dependent samples it showed that there were significant differences in the mean achievement of the students before and after the treatment (using CAI). The identified ISD model used in the development of the tutorial courseware was the ADDIE model. The quantitative analyses of data based on ratings given by the respondents’ shows that the tutorial courseware possess the characteristics and or qualities of a very good computer-based courseware. The ratings given by the different evaluators with regard to content, instructional, and technical aspects of the Tutorial Courseware are in conformity towards being excellent. Students performed better in mathematics, biology chemistry, physics and the English Communication Arts after they were exposed to the tutorial courseware.

Keywords: CAI, tutorial courseware, Instructional Systems Design (ISD) Model, education

Procedia PDF Downloads 304

Authors: Ravi Raj, Louis Chiu, Deepak Marla, Aijun Huang

Abstract:

Hybridization of the directed laser deposition (DLD) process using an in-situ micro-roller to impart a vertical compressive load on the deposited bead at elevated temperatures can relieve tensile residual stresses incurred in the process. To investigate this stress relief mechanism and its relationship with the in-situ rolling parameters, a fully coupled dynamic thermo-mechanical model is presented in this study. A single bead deposition of Ti-6Al-4V alloy with an in-situ roller made of mild steel moving at a constant speed with a fixed nominal bead reduction is simulated using the explicit solver of the finite element software, Abaqus. The thermal model includes laser heating during the deposition process and the heat transfer between the roller and the deposited bead. The laser heating is modeled using a moving heat source with a Gaussian distribution, applied along the pre-formed bead’s surface using the VDFLUX Fortran subroutine. The bead’s cross-section is assumed to be semi-elliptical. The interfacial heat transfer between the roller and the bead is considered in the model. Besides, the roller is cooled internally using axial water flow, considered in the model using convective heat transfer. The mechanical model for the bead and substrate includes the effects of rolling along with the deposition process, and their elastoplastic material behavior is captured using the J2 plasticity theory. The model accounts for strain, strain rate, and temperature effects on the yield stress based on Johnson-Cook’s theory. Various aspects of this material behavior are captured in the FE software using the subroutines -VUMAT for elastoplastic behavior, VUHARD for yield stress, and VUEXPAN for thermal strain. The roller is assumed to be elastic and does not undergo any plastic deformation. Also, contact friction at the roller-bead interface is considered in the model. Based on the thermal results of the bead, the distance between the roller and the deposition nozzle (roller o set) can be determined to ensure rolling occurs around the beta-transus temperature for the Ti-6Al-4V alloy. It is identified that roller offset and the nominal bead height reduction are crucial parameters that influence the residual stresses in the hybrid process. The results obtained from a simulation at roller offset of 20 mm and nominal bead height reduction of 7% reveal that the tensile residual stresses decrease to about 52% due to in-situ rolling throughout the deposited bead. This model can be used to optimize the rolling parameters to minimize the residual stresses in the hybrid DLD process with in-situ micro-rolling.

Keywords: directed laser deposition, finite element analysis, hybrid in-situ rolling, thermo-mechanical model

Procedia PDF Downloads 74

Authors: Luiza Melo De Lima, Tito Trindade, Jose M. Oliveira

Abstract:

The development of thermoplastic-based graphene nanocomposites has been of great interest not only to the scientific community but also to different industrial sectors. Due to the possible improvement of performance and weight reduction, thermoplastic nanocomposites are a great promise as a new class of materials. These nanocomposites are of relevance for the automotive industry, namely because the emission limits of CO2 emissions imposed by the European Commission (EC) regulations can be fulfilled without compromising the car’s performance but by reducing its weight. Thermoplastic polymers have some advantages over thermosetting polymers such as higher productivity, lower density, and recyclability. In the automotive industry, for example, poly(propylene) (PP) is a common thermoplastic polymer, which represents more than half of the polymeric raw material used in automotive parts. Graphene-based materials (GBM) are potential nanofillers that can improve the properties of polymer matrices at very low loading. In comparison to other composites, such as fiber-based composites, weight reduction can positively affect their processing and future applications. However, the properties and performance of GBM/polymer nanocomposites depend on the type of GBM and polymer matrix, the degree of dispersion, and especially the type of interactions between the fillers and the polymer matrix. In order to take advantage of the superior mechanical strength of GBM, strong interfacial strength between GBM and the polymer matrix is required for efficient stress transfer from GBM to the polymer. Thus, chemical compatibilizers and physicochemical modifications have been reported as important tools during the processing of these nanocomposites. In this study, PP-based nanocomposites were obtained by a simple melt blending technique, using a Brabender type mixer machine. Graphene nanoplatelets (GnPs) were applied as structural reinforcement. Two compatibilizers were used to improve the interaction between PP matrix and GnPs: PP graft maleic anhydride (PPgMA) and PPgMA modified with tertiary amine alcohol (PPgDM). The samples for tensile and Charpy impact tests were obtained by injection molding. The results suggested the GnPs presence can increase the mechanical strength of the polymer. However, it was verified that the GnPs presence can promote a decrease of impact resistance, turning the nanocomposites more fragile than neat PP. The compatibilizers’ incorporation increases the impact resistance, suggesting that the compatibilizers can enhance the adhesion between PP and GnPs. Compared to neat PP, Young’s modulus of non-compatibilized nanocomposite increase demonstrated that GnPs incorporation can promote a stiffness improvement of the polymer. This trend can be related to the several physical crosslinking points between the PP matrix and the GnPs. Furthermore, the decrease of strain at a yield of PP/GnPs, together with the enhancement of Young’s modulus, confirms that the GnPs incorporation led to an increase in stiffness but to a decrease in toughness. Moreover, the results demonstrated that incorporation of compatibilizers did not affect Young’s modulus and strain at yield results compared to non-compatibilized nanocomposite. The incorporation of these compatibilizers showed an improvement of nanocomposites’ mechanical properties compared both to those the non-compatibilized nanocomposite and to a PP sample used as reference.

Keywords: graphene nanoplatelets, mechanical properties, melt blending processing, poly(propylene)-based nanocomposites

Procedia PDF Downloads 158

Authors: I. M. L. Nadeesha Jayaweera, Adao Alex Trindade

Abstract:

In choosing a candidate model in likelihood-based modeling via an information criterion, the practitioner is often faced with the difficult task of deciding just how far up the ranked list to look. Motivated by this pragmatic necessity, we construct an uncertainty band for a generalized (model selection) information criterion (GIC), defined as a criterion for which the limit in probability is identical to that of the normalized log-likelihood. This includes common special cases such as AIC & BIC. The method starts from the asymptotic normality of the GIC for the joint distribution of the candidate models in an independent and identically distributed (IID) data framework and proceeds by deriving the (asymptotically) exact distribution of the minimum. The calculation of an upper quantile for its distribution then involves the computation of multivariate Gaussian integrals, which is amenable to efficient implementation via the R package "mvtnorm". The performance of the methodology is tested on simulated data by checking the coverage probability of nominal upper quantiles and compared to the bootstrap. Both methods give coverages close to nominal for large samples, but the bootstrap is two orders of magnitude slower. The methodology is subsequently extended to two other commonly used model structures: regression and time series. In the regression case, we derive the corresponding asymptotically exact distribution of the minimum GIC invoking Lindeberg-Feller type conditions for triangular arrays and are thus able to similarly calculate upper quantiles for its distribution via multivariate Gaussian integration. The bootstrap once again provides a default competing procedure, and we find that similar comparison performance metrics hold as for the IID case. The time series case is complicated by far more intricate asymptotic regime for the joint distribution of the model GIC statistics. Under a Gaussian likelihood, the default in most packages, one needs to derive the limiting distribution of a normalized quadratic form for a realization from a stationary series. Under conditions on the process satisfied by ARMA models, a multivariate normal limit is once again achieved. The bootstrap can, however, be employed for its computation, whence we are once again in the multivariate Gaussian integration paradigm for upper quantile evaluation. Comparisons of this bootstrap-aided semi-exact method with the full-blown bootstrap once again reveal a similar performance but faster computation speeds. One of the most difficult problems in contemporary statistical methodological research is to be able to account for the extra variability introduced by model selection uncertainty, the so-called post-model selection inference (PMSI). We explore ways in which the GIC uncertainty band can be inverted to make inferences on the parameters. This is being attempted in the IID case by pivoting the CDF of the asymptotically exact distribution of the minimum GIC. For inference one parameter at a time and a small number of candidate models, this works well, whence the attained PMSI confidence intervals are wider than the MLE-based Wald, as expected.

Keywords: model selection inference, generalized information criteria, post model selection, Asymptotic Theory

Procedia PDF Downloads 60

Authors: Ana L. M. Pavan, Guilherme Giacomini, Allan F. F. Alves, Marcela De Oliveira, Fernando A. B. Neto, Maria E. D. Rosa, Andre P. Trindade, Diana R. De Pina

Abstract:

Mammography is a worldwide image modality used to diagnose breast cancer, even in asymptomatic women. Due to its large availability, mammograms can be used to measure breast density and to predict cancer development. Women with increased mammographic density have a four- to sixfold increase in their risk of developing breast cancer. Therefore, studies have been made to accurately quantify mammographic breast density. In clinical routine, radiologists perform image evaluations through BIRADS (Breast Imaging Reporting and Data System) assessment. However, this method has inter and intraindividual variability. An automatic objective method to measure breast density could relieve radiologist’s workload by providing a first aid opinion. However, pectoral muscle is a high density tissue, with similar characteristics of fibroglandular tissues. It is consequently hard to automatically quantify mammographic breast density. Therefore, a pre-processing is needed to segment the pectoral muscle which may erroneously be quantified as fibroglandular tissue. The aim of this work was to develop an automatic algorithm to segment and extract pectoral muscle in digital mammograms. The database consisted of thirty medio-lateral oblique incidence digital mammography from São Paulo Medical School. This study was developed with ethical approval from the authors’ institutions and national review panels under protocol number 3720-2010. An algorithm was developed, in Matlab® platform, for the pre-processing of images. The algorithm uses image processing tools to automatically segment and extract the pectoral muscle of mammograms. Firstly, it was applied thresholding technique to remove non-biological information from image. Then, the Hough transform is applied, to find the limit of the pectoral muscle, followed by active contour method. Seed of active contour is applied in the limit of pectoral muscle found by Hough transform. An experienced radiologist also manually performed the pectoral muscle segmentation. Both methods, manual and automatic, were compared using the Jaccard index and Bland-Altman statistics. The comparison between manual and the developed automatic method presented a Jaccard similarity coefficient greater than 90% for all analyzed images, showing the efficiency and accuracy of segmentation of the proposed method. The Bland-Altman statistics compared both methods in relation to area (mm²) of segmented pectoral muscle. The statistic showed data within the 95% confidence interval, enhancing the accuracy of segmentation compared to the manual method. Thus, the method proved to be accurate and robust, segmenting rapidly and freely from intra and inter-observer variability. It is concluded that the proposed method may be used reliably to segment pectoral muscle in digital mammography in clinical routine. The segmentation of the pectoral muscle is very important for further quantifications of fibroglandular tissue volume present in the breast.

Keywords: active contour, fibroglandular tissue, hough transform, pectoral muscle

Procedia PDF Downloads 321