Parâmetros microbiológicos como indicadores de qualidade do solo sob diferentes manejos.

FERTBIO 2010

Fauna invertebrada epigéica em solo cultivado com adubos verdes em Mato Grosso do Sul.

FERTBIO 2010

Biomassa microbiana do solo em cultivo de milho precedido por diferentes espécies de cobertura vegetal, com suprimento de nitrogênio mineral.

FERTBIO 2010

Efeito da queima de palhada da cana-de-açúcar em atributos microbiológicos do solo, em Mato Grosso do Sul.

FERTBIO 2010

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

Histological and immunohistochemical features suggesting aetiological differences in lymph node and (muco)cutaneous feline tuberculosis lesions

Objectives To identify and describe histological and immunohistochemical criteria that may differentiate between skin and lymph node lesions associated with Mycobacterium (M.) bovis and M. microti in a diagnostic pathology setting.Materials and Methods<jats:p/>Archived skin and lymph node biopsies of tuberculous lesions were stained with haematoxylin and eosin, Ziehl‐Neelsen and Masson's Trichrome. Immunohistochemistry was performed to detect the expression of calprotectin, CD3 and Pax5. Samples were scored for histological parameters (i.e. granulomas with central necrosis versus small granulomas without central necrosis, percentage necrosis and/or multinucleated giant cells), number of acid‐fast bacilli (bacterial index) and lesion percentage of fibrosis and positive immunohistochemical staining.Results Twenty‐two samples were examined (M. bovis n=11, M. microti n=11). When controlling for age, gender and tissue, feline M. bovis‐associated lesions more often featured large multi‐layered granulomas with central necrosis. Conversely, this presentation was infrequent in feline M. microti‐associated lesions, where small granulomas without central necrosis predominated. The presence of an outer fibrous capsule was variable in both groups, as was the bacterial index. There were no differences in intralesional expression of immunohistochemical markers.Clinical Significance Differences in the histological appearance of skin and lymph node lesions may help to infer feline infection with either M. bovis or M. microti at an earlier stage when investigating these cases, informing clinicians of the potential zoonotic risk. Importantly, cases of tuberculosis can present with numerous acid‐fast bacilli. This implies that a high bacterial index does not infer infection with non‐zoonotic non‐tuberculous mycobacteria

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms