Fractal descriptors for discrimination of microscopy images of plant leaves

This study proposes the application of fractal descriptors method to the discrimination of microscopy images of plant leaves. Fractal descriptors have demonstrated to be a powerful discriminative method in image analysis, mainly for the discrimination of natural objects. In fact, these descriptors express the spatial arrangement of pixels inside the texture under different scales and such arrangements are directly related to physical properties inherent to the material depicted in the image. Here, we employ the Bouligand-Minkowski descriptors. These are obtained by the dilation of a surface mapping the gray-level texture. The classification of the microscopy images is performed by the well-known Support Vector Machine (SVM) method and we compare the success rate with other literature texture analysis methods. The proposed method achieved a correctness rate of 89%, while the second best solution, the Co-occurrence descriptors, yielded only 78%. This clear advantage of fractal descriptors demonstrates the potential of such approach in the analysis of the plant microscopy images.

Self-perception, complaints and vocal quality among undergraduate students enrolled in a Pedagogy course

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The relationship between obesity, low back pain, and lumbar disc degeneration when genetics and the environment are considered: a systematic review of twin studies

BACKGROUND CONTEXT: The relationships between obesity and low back pain (LBP) and lumbar disc degeneration (LDD) remain unclear. It is possible that familial factors, including genetics and early environment, affect these relationships.PURPOSE: To investigate the relationship between obesity-related measures (eg, weight, body mass index [BMI]) and LBP and LDD using twin studies, where the effect of genetics and early environment can be controlled.STUDY DESIGN: A systematic review with meta-analysis.METHODS: MEDLINE, CINAHL, Scopus, Web of Science, and EMBASE databases were searched from the earliest records to August 2014. All cross-sectional and longitudinal observational twin studies identified by the search strategy were considered for inclusion. Two investigators independently assessed the eligibility, conducted the quality assessment, and extracted the data. Metaanalyses (fixed or random effects, as appropriate) were used to pool studies'estimates of association.RESULTS: In total, 11 articles met the inclusion criteria. Five studies were included in the LBP analysis and seven in the LDD analysis. For the LBP analysis, pooling of the five studies showed that the risk of having LBP for individuals with the highest levels of BMI or weight was almost twice that of people with a lower BMI (odds ratio [OR] 1.8; 95% confidence interval [CI] 1.6-2.0; I-2 = 0%). A dose-response relationship was also identified. When genetics and the effects of a shared early environment were adjusted for using a within-pair twin case-control analysis, pooling of three studies showed a reduced but statistically positive association between obesity and prevalence of LBP (OR 1.5; 95% CI 1.1-2.1; I-2 = 0%). However, the association was further diminished and not significant (OR 1.4; 95% CI 0.8-2.3; I-2 = 0%) when pooling included two studies on monozygotic twin pairs only. Seven studies met the inclusion criteria for LDD. When familial factors were not controlled for, body weight was positively associated with LDD in all five cross-sectional studies. Only two cross-sectional studies investigated the relationship between obesity-related measures and LDD accounting for familial factors, and the results were conflicting. One longitudinal study in LBP and three longitudinal studies in LDD found no increase in risk in obese individuals, whether or not familial factors were controlled for.CONCLUSIONS: Findings from this review suggest that genetics and early environment are possible mechanisms underlying the relationship between obesity and LBP; however, a direct causal link between these conditions appears to be weak. Further longitudinal studies using the twin design are needed to better understand the complex mechanisms underlying the associations between obesity, LBP, and LDD.

A low-power silicon-on-insulator PWM discriminator for biomedical applications

A CMOS/SOI circuit to decode PWM signals is presented as part of a body-implanted neurostimulator for visual prosthesis. Since encoded data is the sole input to the circuit, the decoding technique is based on a double-integration concept and does not require dc filtering. Nonoverlapping control phases are internally derived from the incoming pulses and a fast-settling comparator ensures good discrimination accuracy in the megahertz range. The circuit was integrated on a 2 mu m single-metal SOI fabrication process and has an effective area of 2mm(2) Typically, the measured resolution of encoding parameter a was better than 10% at 6MHz and V-DD=3.3V. Stand-by consumption is around 340 mu W. Pulses with frequencies up to 15MHz and alpha = 10% can be discriminated for V-DD spanning from 2.3V to 3.3V. Such an excellent immunity to V-DD deviations meets a design specification with respect to inherent coupling losses on transmitting data and power by means of a transcutaneous link.

Instantaneous complex power applied to three-phase machines

The theory presented in this paper was primarily developed to give a physical interpretation for the instantaneous power flow on a three-phase induction machine, without a neutral conductor, on any operational state and may be extended to any three-phase load. It is a vectorial interpretation of the instantaneous reactive power theory presented by Akagi et al. Which, believe the authors, isn't enough developed and its physical meaning not yet completely understood. This vectorial interpretation is based on the instantaneous complex power concept defined by Torrens for single-phase, ac, steady-state circuits, and leads to a better understanding of the power phenomenon, particularly of the distortion power. This concept has been extended by the authors to three-phase systems, through the utilization of the instantaneous space vectors. The results of measurements of instantaneous complex power on a self-excited induction generator's terminals, during an over-load application transient, are presented for illustration. The compensation of reactive power proposed by Akagi is discussed and a new horizon for the theory application is opened.

Prospecting major genes in dairy buffaloes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A novel single-phase ZCS-PWM high power factor boost rectifier

This paper presents a novel single-phase high power factor PWM boost rectifier, featuring soft commutation of the active switches at zero-current (ZCS). It incorporates the most desirable properties of the conventional PWM and the soft-switching resonant techniques. The input current shaping is achieved with average current mode control, and continuous inductor current mode. This new PWM converter provides ZCS turn-on and turn-off of the active switches, and it is suitable for high power applications employing IGBTs. Principle of operation, theoretical analysis, a design example, and experimental results from a laboratory prototype rated at 1600 W with 400 Vdc output voltage are presented. The measured efficiency and power factor were 96.2% and 0.99 respectively, with an input current THD equal to 3.94%, for an input voltage THD equal to 3.8%, at rated load.

Análise de formas planas em imagens digitais

Given the widespread use of computers, the visual pattern recognition task has been automated in order to address the huge amount of available digital images. Many applications use image processing techniques as well as feature extraction and visual pattern recognition algorithms in order to identify people, to make the disease diagnosis process easier, to classify objects, etc. based on digital images. Among the features that can be extracted and analyzed from images is the shape of objects or regions. In some cases, shape is the unique feature that can be extracted with a relatively high accuracy from the image. In this work we present some of most important shape analysis methods and compare their performance when applied on three well-known shape image databases. Finally, we propose the development of a new shape descriptor based on the Hough Transform.

As relações entre capacitação de pessoas e operação de tecnologias agrícolas em empresas do setor sucroenergético

Pós-graduação em Agronegócio e Desenvolvimento - Tupã

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Increased IgE serum levels are unrelated to allergic and parasitic diseases in patients with juvenile systemic lupus erythematosus

OBJECTIVE: The aim of this study was to assess the IgE serum levels in juvenile systemic lupus erythematosus patients and to evaluate possible associations with clinical and laboratory features, disease activity and tissue damage. METHODS: The IgE serum concentrations in 69 consecutive juvenile systemic lupus erythematosus patients were determined by nephelometry. IgG, IgM and IgA concentrations were measured by immunoturbidimetry. All patients were negative for intestinal parasites. Statistical analysis methods included the Mann-Whitney, chi-square and Fisher's exact tests, as well as the Spearman rank correlation coefficient. RESULTS: Increased IgE concentrations above 100 IU/mL were observed in 31/69 (45%) juvenile systemic lupus erythematosus patients. The mean IgE concentration was 442.0 +/- 163.4 IU/ml (range 3.5- 9936.0 IU/ml). Fifteen of the 69 patients had atopic disease, nine patients had severe sepsis and 56 patients presented with nephritis. The mean IgE level in 54 juvenile systemic lupus erythematosus patients without atopic manifestations was 271.6 +/- 699.5 IU/ml, and only nine of the 31 (29%) patients with high IgE levels had atopic disease. The IgE levels did not statistically differ with respect to the presence of atopic disease, severe sepsis, nephritis, disease activity, or tissue damage. Interestingly, IgE concentrations were inversely correlated with C4 levels ( r = -0.25, p = 0.03) and with the SLICC/ACR-DI score (r = -0.34, p = 0.005). The IgE concentration was also found to be directly correlated with IgA levels (r = 0.52, p = 0.03). CONCLUSIONS: The present study demonstrated for the first time that juvenile systemic lupus erythematosus patients have increased IgE serum levels. This increase in IgE levels was not related to allergic or parasitic diseases. Our results are in line with the hypothesis that high IgE levels can be considered a marker of immune dysregulation.

Family Management Style Framework and Its Use With Families Who Have a Child Undergoing Palliative Care at Home

Palliative care settings in many countries acknowledge families as their prime focus of care, but in Brazil, to date, researchers have devoted scant attention to that practice setting. In this article, we report the findings of a study that explored how families define and manage their lives when they have a child or adolescent undergoing palliative care at home. Data included individual semistructured interviews with 14 family members of 11 different families. Interviews were transcribed and the coding procedure featured qualitative content analysis methods. The deductive coding was based on the major components of the Family Management Style Framework and the eight dimensions comprising these components. The analysis provides insight into families' daily practices and problems inherent in managing their everyday lives that are encountered when they have a child in palliative care. The article features discussion of implications for the palliative care related development of family nursing practice.

Transcriptional Alterations Related to Neuropathology and Clinical Manifestation of Alzheimer's Disease

Alzheimer's disease (AD) is the most common cause of dementia in the human population, characterized by a spectrum of neuropathological abnormalities that results in memory impairment and loss of other cognitive processes as well as the presence of non-cognitive symptoms. Transcriptomic analyses provide an important approach to elucidating the pathogenesis of complex diseases like AD, helping to figure out both pre-clinical markers to identify susceptible patients and the early pathogenic mechanisms to serve as therapeutic targets. This study provides the gene expression profile of postmortem brain tissue from subjects with clinic-pathological AD (Braak IV, V, or V and CERAD B or C; and CDR >= 1), preclinical AD (Braak IV, V, or VI and CERAD B or C; and CDR = 0), and healthy older individuals (Braak <= II and CERAD 0 or A; and CDR = 0) in order to establish genes related to both AD neuropathology and clinical emergence of dementia. Based on differential gene expression, hierarchical clustering and network analysis, genes involved in energy metabolism, oxidative stress, DNA damage/repair, senescence, and transcriptional regulation were implicated with the neuropathology of AD; a transcriptional profile related to clinical manifestation of AD could not be detected with reliability using differential gene expression analysis, although genes involved in synaptic plasticity, and cell cycle seems to have a role revealed by gene classifier. In conclusion, the present data suggest gene expression profile changes secondary to the development of AD-related pathology and some genes that appear to be related to the clinical manifestation of dementia in subjects with significant AD pathology, making necessary further investigations to better understand these transcriptional findings on the pathogenesis and clinical emergence of AD.

STATE INSTITUTIONS, POWER, AND SOCIAL NETWORKS IN BRAZILIAN URBAN POLICIES

A large historiographic tradition has studied the Brazilian state, yet we know relatively little about its internal dynamics and particularities. The role of informal, personal, and unintentional ties has remained underexplored in most policy network studies, mainly because of the pluralist origin of that tradition. It is possible to use network analysis to expand this knowledge by developing mesolevel analysis of those processes. This article proposes an analytical framework for studying networks inside policy communities. This framework considers the stable and resilient patterns that characterize state institutions, especially in contexts of low institutionalization, particularly those found in Latin America and Brazil. The article builds on research on urban policies in Brazil to suggest that networks made of institutional and personal ties structure state organizations internally and insert them,into broader political scenarios. These networks, which I call state fabric, frame politics, influence public policies, and introduce more stability and predictability than the majority of the literature usually considers. They also form a specific power resource-positional power, associated with the positions that political actors occupy-that influences politics inside and around the state.