Erosive potential of rains in the climate change scenarios in the upper Taquari River Basin, MS, Brazil.

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Detecting the presence and concentration of nitrate in water using microwave spectroscopy

Nitrate is a common pollutant in surface waters which water companies must monitor for regulatory and safety reasons. The presence of nitrate in deionised water is detected and concentration estimated from microwave spectroscopy measurements in the range 9kHz-6GHz. Experimental results were obtained for 19 solutions (18 salt solutions in deionised water and 1 deionised water), each measured 10 times with 4001 points (total N=190). The resulting data was randomly assigned into equal parts training and test data (N=95 each). Both regression (for the estimation of nitrate concentration) and classification (for detecting the presence of nitrate) methods were considered, with a rigorous feature selection procedure used to identify two frequencies for each of the classification and regression problems. For detection classification models were applied with nitrate levels binned using 30mg/l as the threshold. A logistic regression model achieved AUROC of 0.9875 on test data and a multi-layer perceptron achieved AUROC of 0.9871. In each case the positive predictive value of the model could be optimised at 100% with sensitivity of 90% and specificity of 100%. For the concentration estimates, a linear regression model was able to explain 42% of the variance in the training data and 45% of the variance in the test data and an MLP model delivered similar performance, explaining 43% of variance in the training data and 47% of variance in the test data. A sensor based on this model would be appropriate for detecting the presence of nitrate above a given threshold but poor at estimating concentration

Insetos pragas de madeiras de edificações em Belém - Pará.

bitstream/item/32005/1/CPATU-BP101.pd

Relating pseudospin and spin symmetries through charge conjugation and chiral transformations: the case of the relativistic harmonic oscillator

We solve the generalized relativistic harmonic oscillator in 1+1 dimensions, i.e., including a linear pseudoscalar potential and quadratic scalar and vector potentials which have equal or opposite signs. We consider positive and negative quadratic potentials and discuss in detail their bound-state solutions for fermions and antifermions. The main features of these bound states are the same as the ones of the generalized three-dimensional relativistic harmonic oscillator bound states. The solutions found for zero pseudoscalar potential are related to the spin and pseudospin symmetry of the Dirac equation in 3+1 dimensions. We show how the charge conjugation and $\gamma^5$ chiral transformations relate the several spectra obtained and find that for massless particles the spin and pseudospin symmetry related problems have the same spectrum, but different spinor solutions. Finally, we establish a relation of the solutions found with single-particle states of nuclei described by relativistic mean-field theories with scalar, vector and isoscalar tensor interactions and discuss the conditions in which one may have both nucleon and antinucleon bound states.Comment: 33 pages, 10 figures, uses revtex macro

Self-reported clinical history of misdiagnosed leprosy cases in the State of Mato Grosso, Brzil, 2016-2019

This study aimed to analyze the self-reported clinical history of patients misdiagnosed with leprosy in the State of Mato Grosso, Brazil. This is a cross-sectional study of new leprosy cases diagnosed in the State of Mato Grosso from 2016 to 2019, with individuals who were released from multidrug therapy due to misdiagnosis after starting treatment. Data were collected via telephone interviews. Over the study period, 354 leprosy cases were released from treatment due to misdiagnosis, of which 162 (45.8%) could be interviewed. All interviewees expressed dissatisfaction with their treatment, which prompted them to seek a reevaluation of their diagnosis before they were released due to "misdiagnosis". Among them, 35.8% received a final diagnosis of a musculoskeletal or connective tissue disease - mainly fibromyalgia and degenerative changes in the spine - followed by 13.6% with diagnoses of skin and subcutaneous tissue diseases. For 23.5% of the respondents, no alternative diagnosis was established, whereas 7.4% were later re-diagnosed with leprosy. Fibromyalgia and spinal problems were the most common alternative diagnoses for erroneous leprosy. Although the diagnosis of leprosy is usually clinical and does not require access to technical infrastructure in most cases, some more complex situations require diagnostic support via complementary tests, as well as close collaboration between primary care and reference services

Use of q-values to Improve a Genetic Algorithm to Identify Robust Gene Signatures

Several approaches have been proposed for the analysis of DNA microarray datasets, focusing on the performance and robustness of the final feature subsets. The novelty of this paper arises in the use of q-values to pre-filter the features of a DNA microarray dataset identifying the most significant ones and including this information into a genetic algorithm for further feature selection. This method is applied to a lung cancer microarray dataset resulting in similar performance rates and greater robustness in terms of selected features (on average a 36.21% of robustness improvement) when compared to results of the standard algorithm

GAGs-thiolated chitosan assemblies for chronic wounds treatment: control of enzyme activity and cell attachment

Multilayered polyelectrolyte coatings comprising thiolated chitosan (TC) and glycosaminoglycans (GAGs), chondroitin sulphate and hyaluronic acid, were built using a layer-by-layer approach. The surface activity of these coatings for binding and inhibition of enzymes related to chronic inflammation, such as collagenase and myeloperoxidase, was assessed. The build-up of five bi-layers of TC/GAGs onto gold surfaces was monitored in situ by QCM-D. All experimental groups showed exponential growth of the coatings controlled by the degree of chitosan thiolation and the molecular weight of the GAGs. The degree of chitosan modification was also the key parameter influencing the enzyme activity: increasing the thiols content led to more efficient myeloperoxidase inhibition and was inversely proportional to the adsorption of collagenase. Enhanced fibroblast attachment and proliferation were observed when the multilayered polyelectrolyte constructs terminated with GAGs. The possibility to control either the activity of major wound enzymes by the thiolation degree of the coating or the cell adhesion and proliferation by proper selection of the ultimate layer makes these materials potentially useful in chronic wounds treatment and dermal tissue regeneration.EU projects Lidwine (contract no. FP6-026741)Ministerio de Ciencia e Innovaci on de España - (MICINN) - bolsa BES-2008-0037

Energetic potential of Sclerobium paniculatum vogel (taxi-branco) and its preferential top climatic condition in Eastern Amazonia, Brasil.

Tropentag 2009

Societal issues in machine learning: When learning from data is not enough

It has been argued that Artificial Intelligence (AI) is experiencing a fast process of commodification. Such characterization is on the interest of big IT companies, but it correctly reflects the current industrialization of AI. This phenomenon means that AI systems and products are reaching the society at large and, therefore, that societal issues related to the use of AI and Machine Learning (ML) cannot be ignored any longer. Designing ML models from this human-centered perspective means incorporating human-relevant requirements such as safety, fairness, privacy, and interpretability, but also considering broad societal issues such as ethics and legislation. These are essential aspects to foster the acceptance of ML-based technologies, as well as to ensure compliance with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. The ESANN special session for which this tutorial acts as an introduction aims to showcase the state of the art on these increasingly relevant topics among ML theoreticians and practitioners. For this purpose, we welcomed both solid contributions and preliminary relevant results showing the potential, the limitations and the challenges of new ideas, as well as refinements, or hybridizations among the different fields of research, ML and related approaches in facing real-world problems involving societal issues