CLIMATE CHANGE ADAPTATION IN VULNERABLE CROP AND LIVESTOCK PRODUCTION SYSTEMS IN MGETA, TANZANIA

Increased occurrence of drought and dry spells during the growing season have resulted in increased interest in protection of tropical water catchment areas. In Mgeta, a water catchment area in the Uluguru Mountains in Tanzania, water used for vegetable and fruit production is provided through canals from the Uluguru South Forest Reserve. The clearing of forest land for cultivation in the steep slopes in the area is causing severe land degradation, which is threatening the water catchment area, livelihoods, and food security of the local communities, as well as the major population centers in the lowlands. In this paper, the economic performance of a traditional cropping-livestock system with East African (EA)-goats and pigs and extensive vegetable production is compared with a more sustainable and environmentally friendly crop-dairy goat production system. A linear programming (LP) crop-livestock model, maximizing farm income considering the environmental constraints in the area was applied for studying the economic performance of dairy goats in the production system. The model was worked out for the rainy and dry seasons and the analysis was conducted for a basic scenario representing the current situation, based on the variability in the 30 years period from 1982-2012, and in a scenario of both lower crop yields and increased crop variability due to climate change. Data obtained from a sample of 60 farmers that were interviewed using a questionnaire was used to develop and parameterize the model. The study found that in the steep slopes of the area, a crop-dairy goat system with extensive use of grass and multipurpose trees (MPTs) would do better than the traditional vegetable gardening with the EA goat production system. The crop-dairy goat system was superior both in the basic and in a climate change scenario since the yield variation of the grass and MPTs system was less affected compared to vegetable crops due to more tree cover and the use of perennial grasses. However, the goat milk production in the area was constrained by inadequate feeding and lack of an appropriate breeding program. Hence, farmers should enhance goat milk production by supplementing with more concentrate feed and by implementing goat-breeding principles. Moreover, policy measures to promote such a development are briefly discussed.

Amazon forest structure generates diurnal and seasonal variability in light utilization.

The complex three-dimensional (3-D) structure of tropical forests generates a diversity of light environments for canopy and understory trees. Understanding diurnal and seasonal changes in light availability is critical for interpreting measurements of net ecosystem exchange and improving ecosystem models. Here, we used the Discrete Anisotropic Radiative Transfer (DART) model to simulate leaf absorption of photosynthetically active radiation (lAPAR) for an Amazon forest. The 3-D model scene was developed from airborne lidar data, and local measurements of leaf reflectance, aerosols, and PAR were used to model lAPAR under direct and diffuse illumination conditions. Simulated lAPAR under clear-sky and cloudy conditions was corrected for light saturation effects to estimate light utilization, the fraction of lAPAR available for photosynthesis. Although the fraction of incoming PAR absorbed by leaves was consistent throughout the year (0.80?0.82), light utilization varied seasonally (0.67?0.74), with minimum values during the Amazon dry season. Shadowing and light saturation effects moderated potential gains in forest productivity from increasing PAR during dry-season months when the diffuse fraction from clouds and aerosols was low. Comparisons between DART and other models highlighted the role of 3-D forest structure to account for seasonal changes in light utilization. Our findings highlight how directional illumination and forest 3-D structure combine to influence diurnal and seasonal variability in light utilization, independent of further changes in leaf area, leaf age, or environmental controls on canopy photosynthesis. Changing illumination geometry constitutes an alternative biophysical explanation for observed seasonality in Amazon forest productivity without changes in canopy phenology.

Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian Semiarid region, for use in Dynamic Global Vegetation Models.

The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga

Molecular simulations and modeling of pharmaceutically relevant RNA-processing enzymes

The two-metal-ion architecture is a structural feature found in a variety of RNA processing metalloenzymes or ribozymes (RNA-based enzymes), which control the biogenesis and the metabolism of vital RNAs, including non-coding RNAs (ncRNAs). Notably, such ncRNAs are emerging as key players for the regulation of cellular homeostasis, and their altered expression has been often linked to the development of severe human pathologies, from cancer to mental disorders. Accordingly, understanding the biological processing of ncRNAs is foundational for the development of novel therapeutic strategies and tools. Here, we use state-of the-art molecular simulations, complemented with X-ray crystallography and biochemical experiments, to characterize the RNA processing cycle as catalyzed by two two-metal-ion enzymes: the group II intron ribozymes and the RNase H1. We show that multiple and diverse cations are strategically recruited at and timely released from the enzymes’ active site during catalysis. Such a controlled cations’ trafficking leads to the recursive formation and disruption of an extended two-metal ion architecture that is functional for RNA-hydrolysis – from substrate recruitment to product release. Importantly, we found that these cations’ binding sites are conserved among other RNA-processing machineries, including the human spliceosome and CRISPR-Cas systems, suggesting that an evolutionarily-converged catalytic strategy is adopted by these enzymes to process RNA molecules. Thus, our findings corroborate and sensibly extend the current knowledge of two-metal-ion enzymes, and support the design of novel drugs targeting RNA-processing metalloenzymes or ribozymes as well as the rational engineering of novel programmable gene-therapy tools.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

P16(ink) (4a) Expression As A Potential Marker Of Low-grade Cervical Intraepithelial Neoplasia Progression.

To evaluate p16(INK) (4a) immunoexpression in CIN1 lesions looking for differences between cases that progress to CIN2/3 maintain CIN1 diagnosis, or spontaneously regress. Seventy-four CIN1 biopsies were studied. In the follow-up, a second biopsy was performed and 28.7% showed no lesion (regression), 37.9% maintained CIN1, and 33.4% progressed to CIN2/3. Immunostaining for p16(INK) (4a) was performed in the first biopsy and it was considered positive when there was strong and diffuse staining of the basal and parabasal layers. Pearson's chi-square was used to compare the groups (p ≤ 0.05). The age of the patients was similar. There was no significant difference in p16(INK) (4a) immunoexpression in the groups, however, statistical analyses showed a significant association when only the progression and regression groups were compared (p = 0.042). Considering p16(INK) (4a) positivity and the progression to CIN2/3, the sensitivity, specificity, positive, and negative predictive values in our cohort were 45%, 75%, 47%, and 94%, respectively. We emphasize that CIN1 with p16(INK) (4a) staining was associated with lesion progression, but the sensitivity was not high. However, the negative predictive value was more reliable (94%) and p16(INK) (4a) may represent a useful biomarker that can identify CIN1 lesions that need particular attention, complementing morphology.

Adiposidade em adolescentes e obesidade materna

OBJETIVO: Descrever a relação entre adiposidade na adolescência e obesidade materna. MÉTODOS: Foi realizado estudo transversal com 660 indivíduos de 8 a 18 anos, de ambos os sexos, matriculados em uma escola pública e outra privada do município de São Paulo. A coleta de dados foi realizada por meio de entrevista, medidas antropométricas e inquérito alimentar. A adiposidade na adolescência foi mensurada a partir do índice de massa corporal e, por meio de análise de regressão, verificou-se sua relação com a obesidade materna, ajustada por sexo, idade, estágio de maturação sexual, valor energético total da dieta, atividade física, sedentarismo, peso ao nascer e escolaridade materna. RESULTADOS: Dos adolescentes estudados, 64,7% eram do sexo feminino. A média (desvio-padrão) de idade foi de 12,4 (1,80), variando de 8 a 17 anos. Verificou-se maior prevalência de excesso de peso e obesidade entre os indivíduos do sexo masculino, não sendo observada associação significativa entre estado nutricional e sexo. Após ajuste pelas covariáveis, detectou-se que filhos de mães obesas têm risco quatro vezes maior de ser obesos, quando comparados aos adolescentes filhos de mães não obesas. CONCLUSÃO: Conclui-se que a obesidade materna representa fator de risco importante para o desenvolvimento da obesidade na adolescência.

Association of the borer Spermophthorus apuleiae (Coleoptera; Curculionidae; Scolytinae) with the "pau-ferro" tree Caesalpinia ferrea (Leguminosae)

The weevil subfamily Scolytinae includes beetles which may feed on the bark, trunk or roots of both live and dead trees and are sometimes considered forest and silvicultural pests. Less frequently, some species feed on seeds and may be cause economic losses when associated to plant cultivars. Spermophthorus apuleiae Costa-Lima is a Neotropical Scolytinae formerly recorded to be "associated" with seeds of Caesalpinia ferrea var. leiostachya Benth, a Brazilian tree popularly known in Portuguese as "pau-ferro". Hitherto, it was not clear whether these beetles actually feed on the seeds of that plant. In order to investigate the ability of S. apuleiae to feed on seeds of "pau-ferro", observations were done and colonies of these beetles were established. Both in the field and in captivity the beetles were not observed feeding on the seeds. Even when beetles were exposed to seeds as the only source of food they were incapable of boring or eating the seeds and died. Our data therefore suggest that S. apuleiae is a frugivorous species which peculiarly does not eat seeds of "pau-ferro".

Work disability remains a major problem in rheumatoid arthritis in the 2000s: data from 32 countries in the QUEST-RA Study

Introduction: Work disability is a major consequence of rheumatoid arthritis (RA), associated not only with traditional disease activity variables, but also more significantly with demographic, functional, occupational, and societal variables. Recent reports suggest that the use of biologic agents offers potential for reduced work disability rates, but the conclusions are based on surrogate disease activity measures derived from studies primarily from Western countries. Methods: The Quantitative Standard Monitoring of Patients with RA (QUEST-RA) multinational database of 8,039 patients in 86 sites in 32 countries, 16 with high gross domestic product (GDP) (>24K US dollars (USD) per capita) and 16 low-GDP countries (<11K USD), was analyzed for work and disability status at onset and over the course of RA and clinical status of patients who continued working or had stopped working in high-GDP versus low-GDP countries according to all RA Core Data Set measures. Associations of work disability status with RA Core Data Set variables and indices were analyzed using descriptive statistics and regression analyses. Results: At the time of first symptoms, 86% of men (range 57%-100% among countries) and 64% (19%-87%) of women <65 years were working. More than one third (37%) of these patients reported subsequent work disability because of RA. Among 1,756 patients whose symptoms had begun during the 2000s, the probabilities of continuing to work were 80% (95% confidence interval (CI) 78%-82%) at 2 years and 68% (95% CI 65%-71%) at 5 years, with similar patterns in high-GDP and low-GDP countries. Patients who continued working versus stopped working had significantly better clinical status for all clinical status measures and patient self-report scores, with similar patterns in high-GDP and low-GDP countries. However, patients who had stopped working in high-GDP countries had better clinical status than patients who continued working in low-GDP countries. The most significant identifier of work disability in all subgroups was Health Assessment Questionnaire (HAQ) functional disability score. Conclusions: Work disability rates remain high among people with RA during this millennium. In low-GDP countries, people remain working with high levels of disability and disease activity. Cultural and economic differences between societies affect work disability as an outcome measure for RA.

Near full-length genome analysis of low prevalent human immunodeficiency virus type 1 subclade F1 in Sao Paulo, Brazil

Background: The genetic diversity of the human immunodeficiency virus type 1 (HIV-1) is critical to lay the groundwork for the design of successful drugs or vaccine. In this study we aimed to characterize and define the molecular prevalence of HIV-1 subclade F1 currently circulating in Sao Paulo, Brazil. Methods: A total of 36 samples were selected from 888 adult patients residing in Sao Paulo who had previously been diagnosed in two independent studies in our laboratory as being infected with subclade F1 based on pol subgenomic fragment sequencing. Proviral DNA was amplified from the purified genomic DNA of all 36 blood samples by 5 fragments overlapping PCR followed by direct sequencing. Sequence data were obtained from the 5 fragments of pure subclade F1 and phylogenetic trees were constructed and compared with previously published sequences. Subclades F1 that exhibited mosaic structure with other subtypes were omitted from any further analysis Results: Our methods of fragment amplification and sequencing confirmed that only 5 sequences inferred from pol region as subclade F1 also holds true for the genome as a whole and, thus, estimated the true prevalence at 0.56%. The results also showed a single phylogenetic cluster of the Brazilian subclade F1 along with non-Brazilian South American isolates in both subgenomic and the full-length genomes analysis with an overall intrasubtype nucleotide divergence of 6.9%. The nucleotide differences within the South American and Central African F1 strains, in the C2-C3 env, were 8.5% and 12.3%, respectively. Conclusion: All together, our findings showed a surprisingly low prevalence rate of subclade F1 in Brazil and suggest that these isolates originated in Central Africa and subsequently introduced to South America.

Influence of Post-Clearing Treatment on the Recovery of Herbaceous Plant Communities in Amazonian Secondary Forests

Secondary forests are an increasingly common feature in tropical landscapes worldwide and understanding their regeneration is necessary to design effective restoration strategies. It has previously been shown that the woody species community in secondary forests can follow different successional pathways according to the nature of past human activities in the area, yet little is known about patterns of herbaceous species diversity in secondary forests with different histories of land use. We compared the diversity and abundance of herbaceous plant communities in two types of Central Amazonian secondary forests-those regenerating on pastures created by felling and burning trees and those where trees were felled only. We also tested if plant density and species richness in secondary forests are related to proximity to primary forest. In comparison with primary forest sites, forests regenerating on non-burned habitats had lower herbaceous plant density and species richness than those on burned ones. However, species composition and abundance in non-burned stands were more similar to those of primary forest, whereas several secondary forest specialist species were found in burned stands. In both non-burned and burned forests, distance from the forest edge was not related to herbaceous density and species richness. Overall, our results suggest that the natural regeneration of herbaceous species in secondary tropical forests is dependent on a site`s post-clearing treatment. We recommend evaluating the land history of a site prior to developing and implementing a restoration strategy, as this will influence the biological template on which restoration efforts are overlaid.

MPC for tracking zone regions

This paper deals with the problem of tracking target sets using a model predictive control (MPC) law. Some MPC applications require a control strategy in which some system outputs are controlled within specified ranges or zones (zone control), while some other variables - possibly including input variables - are steered to fixed target or set-point. In real applications, this problem is often overcome by including and excluding an appropriate penalization for the output errors in the control cost function. In this way, throughout the continuous operation of the process, the control system keeps switching from one controller to another, and even if a stabilizing control law is developed for each of the control configurations, switching among stable controllers not necessarily produces a stable closed loop system. From a theoretical point of view, the control objective of this kind of problem can be seen as a target set (in the output space) instead of a target point, since inside the zones there are no preferences between one point or another. In this work, a stable MPC formulation for constrained linear systems, with several practical properties is developed for this scenario. The concept of distance from a point to a set is exploited to propose an additional cost term, which ensures both, recursive feasibility and local optimality. The performance of the proposed strategy is illustrated by simulation of an ill-conditioned distillation column. (C) 2010 Elsevier Ltd. All rights reserved.

Role of healthy nursery plants in orange yield during eight years of Citrus Variegated Chlorosis epidemics

The production and commercialization of citrus seedlings inspected and produced in protected screen-houses has become mandatory in Sao Paulo State, Brazil since January 2003. This law was intended to avoid the dispersion of Citrus Variegated Chlorosis (CVC), disease caused by Xylella fastidiosa. Our objective was to compare the yield over 8 years of `Natal` sweet orange trees grafted onto Rangpur lime obtained from healthy nursery plants and from plants artificially inoculated with X. fastidiosa. Yield was evaluated in an orchard planted in February 1999 with two treatments: (i) trees from healthy nursery plant, and (ii) trees from plants artificially inoculated with X. fastidiosa. The mean yield was 21% higher in trees from healthy nursery plants, as compared to trees from inoculated nursery plants. This difference represents a gain of approximately 203 boxes of 40.8 kg each, considering a planting density of 550 plants per hectare. (C) 2011 Elsevier B.V. All rights reserved.

A comparison of lipid variables as predictors of cardiovascular disease in the Asia Pacific Region

PURPOSE: Many guidelines advocate measurement of total or low density lipoprotein cholesterol (LDL), high density lipoprotein cholesterol (HDL), and triglycerides (TG) to determine treatment recommendations for preventing coronary heart disease (CHD) and cardiovascular disease (CVD). This analysis is a comparison of lipid variables as predictors of cardiovascular disease. METHODS: Hazard ratios for coronary and cardiovascular deaths by fourths of total cholesterol (TC), LDL, HDL, TG, non-HDL, TC/HDL, and TG/HDL values, and for a one standard deviation change in these variables, were derived in an individual participant data meta-analysis of 32 cohort studies conducted in the Asia-Pacific region. The predictive value of each lipid variable was assessed using the likelihood ratio statistic. RESULTS: Adjusting for confounders and regression dilution, each lipid variable had a positive (negative for HDL) log-linear association with fatal CHD and CVD. Individuals in the highest fourth of each lipid variable had approximately twice the risk of CHD compared with those with lowest levels. TG and HDL were each better predictors of CHD and CVD risk compared with TC alone, with test statistics similar to TC/HDL and TG/HDL ratios. Calculated LDL was a relatively poor predictor. CONCLUSIONS: While LDL reduction remains the main target of intervention for lipid-lowering, these data support the potential use of TG or lipid ratios for CHD risk prediction. (c) 2005 Elsevier Inc. All rights reserved.