Form tutors in the joint teaching of music: perspectives and experiences

In the Portuguese school system, form tutors (FTs) are an intermediate education management structure. The form tutor is responsible for a position of coordination and orientation with a «threefold function»: a relationship with students, relationship with the student’s family and relationship with other class teachers. The joint teaching of music is a part of the educational system in basic education. It is optional and provides musical and instrumental training to students who are interested in taking it. In order to achieve this, there is a system, subject to protocols, between general education schools and schools specializing in teaching music. There is also the position of FT in specialized schools and it also includes a «threefold function». However, these FTs have the additional role of representing music teachers at the class council, which is held at the general education school. Thus, in these cases, both FTs have a fourth joint area, between the music school and the general education school, which makes the relationship between them even more complex. This paper is based on the analysis of empirical data obtained from the testimonies of FTs regarding their representations and experiences in leadership and the coordination of teachers among schools in which there is a joint teaching system. The aim of collecting narratives is to look into some of the main challenges and confrontations related to leadership issues, which we assume to be the focus of those who have the role of form tutor in that specific context.

Patterns of Mycobacterium tuberculosis-complex excretion and characterization of super-shedders in naturally-infected wild boar and red deer

Wild boar (Sus scrofa) and red deer (Cervus elaphus) are the main maintenance hosts for bovine tuberculosis (bTB) in continental Europe. Understanding Mycobacterium tuberculosis complex (MTC) excretion routes is crucial to define strategies to control bTB in free-ranging populations, nevertheless available information is scarce. Aiming at filling this gap, four different MTC excretion routes (oronasal, bronchial-alveolar, fecal and urinary) were investigated by molecular methods in naturally infected hunter-harvested wild boar and red deer. In addition MTC concentrations were estimated by the Most Probable Number method. MTC DNA was amplified in all types of excretion routes. MTC DNA was amplified in at least one excretion route from 83.0% (CI95 70.8-90.8) of wild ungulates with bTB-like lesions. Oronasal or bronchial-alveolar shedding were detected with higher frequency than fecal shedding (p < 0.001). The majority of shedders yielded MTC concentrations <10(3) CFU/g or mL. However, from those ungulates from which oronasal, bronchial-alveolar and fecal samples were available, 28.2% of wild boar (CI95 16.6-43.8) and 35.7% of red deer (CI95 16.3-61.2) yielded MTC concentrations >10(3) CFU/g or mL (referred here as super-shedders). Red deer have a significantly higher risk of being super-shedders compared to wild boar (OR = 11.8, CI95 2.3-60.2). The existence of super-shedders among the naturally infected population of wild boar and red deer is thus reported here for the first time and MTC DNA concentrations greater than the minimum infective doses were estimated in excretion samples from both species.

Widespread environmental contamination with Mycobacterium tuberculosis complex revealed by a molecular detection protocol

Environmental contamination with Mycobacterium tuberculosis complex (MTC) has been considered crucial for bovine tuberculosis persistence in multi-host-pathogen systems. However, MTC contamination has been difficult to detect due to methodological issues. In an attempt to overcome this limitation we developed an improved protocol for the detection of MTC DNA. MTC DNA concentration was estimated by the Most Probable Number (MPN) method. Making use of this protocol we showed that MTC contamination is widespread in different types of environmental samples from the Iberian Peninsula, which supports indirect transmission as a contributing mechanism for the maintenance of bovine tuberculosis in this multi-host-pathogen system. The proportion of MTC DNA positive samples was higher in the bovine tuberculosis-infected than in presumed negative area (0.32 and 0.18, respectively). Detection varied with the type of environmental sample and was more frequent in sediment from dams and less frequent in water also from dams (0.22 and 0.05, respectively). The proportion of MTC-positive samples was significantly higher in spring (p<0.001), but MTC DNA concentration per sample was higher in autumn and lower in summer. The average MTC DNA concentration in positive samples was 0.82 MPN/g (CI95 0.70-0.98 MPN/g). We were further able to amplify a DNA sequence specific of Mycobacterium bovis/caprae in 4 environmental samples from the bTB-infected area.

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.

The influence of kaolin application on key metabolic pathways associated with grape berry quality: a molecular and biochemical analysis

Dissertação de mestrado em Biologia Molecular, Biotecnologia e Bioempreendedorismo em Plantas

Associations, causation and model in psychiatry

This paper discusses models, associations and causation in psychiatry. The different types of association (linear, positive, negative, exponential, partial, U shaped relationship, hidden and spurious) between variables involved in mental disorders are presented as well as the use of multiple regression analysis to disentangle interrelatedness amongst multiple variables. A useful model should have internal consistency, external validity and predictive power; be dynamic in order to accommodate new sound knowledge; and should fit facts rather than they other way around. It is argued that whilst models are theoretical constructs they also convey a style of reasoning and can change clinical practice. Cause and effect are complex phenomena in that the same cause can yield different effects. Conversely, the same effect can have a different range of causes. In mental disorders and human behaviour there is always a chain of events initiated by the indirect and remote cause; followed by intermediate causes; and finally the direct and more immediate cause. Causes of mental disorders are grouped as those: (i) which are necessary and sufficient; (ii) which are necessary but not sufficient; and (iii) which are neither necessary nor sufficient, but when present increase the risk for mental disorders.

Systems biology approaches for the design of novel Saccharomyces cerevisiae winemaking strains for enhanced flavour compounds synthesis

Tese de Doutoramento em Biologia Ambiental e Molecular

A glycolytic phenotype is associated with prostate cancer progression and aggressiveness: a role for Monocarboxylate Transporters as metabolic targets for therapy.

Metabolic adaptation is considered an emerging hallmark of cancer, whereby cancer cells exhibit high rates of glucose consumption with consequent lactate production. To ensure rapid efflux of lactate, most cancer cells express high levels of monocarboxylate transporters (MCTs), which therefore may constitute suitable therapeutic targets. The impact of MCT inhibition, along with the clinical impact of altered cellular metabolism during prostate cancer (PCa) initiation and progression, has not been described. Using a large cohort of human prostate tissues of different grades, in silico data, in vitro and ex vivo studies, we demonstrate the metabolic heterogeneity of PCa and its clinical relevance. We show an increased glycolytic phenotype in advanced stages of PCa and its correlation with poor prognosis. Finally, we present evidence supporting MCTs as suitable targets in PCa, affecting not only cancer cell proliferation and survival but also the expression of a number of hypoxia-inducible factor target genes associated with poor prognosis. Herein, we suggest that patients with highly glycolytic tumours have poorer outcome, supporting the notion of targeting glycolytic tumour cells in prostate cancer through the use of MCT inhibitors.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering

Hormonal, metabolic and nutritional alterations in smokers: emergency for smoking abstinence

OBJECTIVE: To evaluate the biochemical and nutritional status of smokers in treatment for smoking cessation and its association with anthropometric parameters. METHODS: This is a cross-sectional study with convenience sample. Adult smokers were assessed at the start of treatment in the Interdisciplinary Center for Tobacco Research and Intervention of the University Hospital of the Federal University of Juiz de Fora (CIPIT/HU-UFJF). We evaluated the body mass index (BMI), conicity index (CI); waist circumference (WC), percentage of body fat (%BF), fasting glycemia, cortisol, insulin, total cholesterol (TC), LDL-c, HDL-c, triglycerides (TG) and metabolic syndrome (MS). RESULTS: Most participants (52.2%) had MS and high cardiovascular risk. The fasting glycemia was abnormal in 30.4%. There was a significant positive correlation between BMI and WC (r = 0.90; p = 0.0001), %BF (r = 0.79; p = 0.0001), CI (r = 0.65; p = 0.0001), glycemia (r = 0.42; p = 0.04) and TG (r = 0.47; p = 0.002). The CI presented positive correction with insulin (r = 0.60; p = 0.001), glycemia (r = 0.55; p = 0.007), TG (r = 0.54; p = 0.008) and %BF (r = 0.43; p = 0.004). Patients with longer duration of smoking had a higher risk of developing MS (OR = 9.6, p = 0.016). CONCLUSION: The smokers evaluated had increased risk for developing MS, especially those with longer duration of smoking, requiring urgent smoking cessation.

Propolis: A complex natural product with a plethora of biological activities that can be explored for drug development

The health industry has always used natural products as a rich, promising, and alternative source of drugs that are used in the health system. Propolis, a natural resinous product known for centuries, is a complex product obtained by honey bees from substances collected from parts of different plants, buds, and exudates in different geographic areas. Propolis has been attracting scientific attention since it has many biological and pharmacological properties, which are related to its chemical composition. Several in vitro and in vivo studies have been performed to characterize and understand the diverse bioactivities of propolis and its isolated compounds, as well as to evaluate and validate its potential. Yet, there is a lack of information concerning clinical effectiveness. The goal of this review is to discuss the potential of propolis for the development of new drugs by presenting published data concerning the chemical composition and the biological properties of this natural compound from different geographic origins.

A critical evaluation of methods for the reconstruction of tissue-specific models

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared. This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.

By-products of beer fermentation

Among the most important factors influencing beer quality is the presence of well-adjusted amounts of higher alcohols and esters; as well as the successful reduction of undesirable by-products such as diacetyl. While higher alcohols and esters contribute rather positively to the beer aroma, diacetyl is mostly unwelcome for beer types with lighter taste. Thus, the complex metabolic pathways in yeast responsible for the synthesis of both pleasant and unpleasant by-products of fermentation were given special attention in this last chapter.

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.