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Large-scale epidemics of arboviruses, such as dengue, have heightened societal awareness regarding the necessity of combating the primary transmission vectors. Equally critical is the identification of environmental conditions and variables that influence vector population dynamics. Aedes aegypti , the primary vector of arboviruses such as dengue and Zika in Brazil, is closely associated with the climatic and geographical conditions of urban environments. This study examines the relationship between precipitation and confirmed dengue cases in Recife (Brazil), employing regression and quantile analyses to evaluate the influence of meteorological conditions on the disease's spread. The findings reveal a direct correlation between monthly averages of precipitation and confirmed cases, although this is apparent only when excluding years of epidemic peaks. The highest number of cases generally aligns with the rainy season, and the lowest with the dry season, with weak, moderate and strong precipitation events being closely linked to increased dengue incidence. However, notable discrepancies were identified: four out of six major outbreaks occurred in drier months, challenging the assumption of a straightforward relationship between rainfall and dengue incidence. These findings underscore the multifaceted nature of dengue dynamics, suggesting that while precipitation plays a significant role, other factors, including serotype circulation and broader climatic phenomena, are equally critical in driving outbreaks. This complexity highlights the need for a more comprehensive understanding of the mechanisms influencing dengue epidemics., Competing Interests: The authors have no relevant financial conflicts of interest. However, it should be noted that Tiago Massoni, Patty Kostkova, Tercio Ambrizzi, and Anwar Musah are members of the Editorial Board of Vector-Borne Diseases -- The Digital One Health Approach. Due to this affiliation, the manuscript has been managed by other editorial members. These author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Borges, Musah, Dutra, Tunali, Lima, Tunali, da Silva, Aldosery, Moreno, dos Santos, Massoni, Yenigün, Kostkova, da Rocha, Campos and Ambrizzi.)

Due to the anthropogenic pressures of expansion areas for livestock and agricultural production in the Brazilian Cerrado, it is of paramount importance to understand the dynamics of land use/land cover (LULC) changes in this region. Thus, we investigated LULC changes in two sub-basins of the Tocantins-Araguaia River basin from 1997 to 2015 and consequently projected future changes for the timespan between 2030 and 2050. The Formoso sub-basin experienced significant expansion of agricultural and pasture areas, whereas the Sono sub-basin limited farmland expansion (more stable native vegetation) due to substantial protected areas, trends that were also observed for future projections (2030 and 2050). Pastureland in the Formoso sub-basin increased by 5.8%, while the Sono sub-basin saw significant gains in cultivated land, according to change detection analyses during the 1997-2015 period. High stability probabilities of no change (> 70%) for grassland areas in the Sono River sub-basin and pasturelands in the Formoso River sub-basin were computed. The CA-Markov model demonstrated a high consistency level with actual LULC classes for both sub-basins, as indicated by an overall Kappa coefficient above 0.8. Future projections for 2030 and 2050 show a substantial expansion of agriculture and pasture in both sub-basins, driven by specific factors such as soil organic carbon stocks, distance from rural settlements, and proximity to rivers. Short- and mid-term simulations indicate substantial expansion of agriculture and pasture in both basins, with potential adverse impacts on water erosion. Consequently, developing policies for soil management and sustainable land use planning is essential for agroecosystem sustainability, promoting a balanced approach to economic development while addressing climate change and anthropogenic challenges., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This study aims to investigate the effects of canning variables (cooking time, storage time, volume of vinegar, salt and sugar) on the mineral composition of canned cowpea ( Vigna unguiculata (L.) Walp) and which conditions provide optimised preservation of the mineral content of the grains. Different formulations of canned cowpeas were produced following two levels factorial experimental design using five variables. A set of 11 different formulations were evaluated using the desirability function with essential minerals (Ca, Cu, Fe, Mn, Mg, P and Zn) as the response. The optimal multi-response conditions for higher mineral retention were: 360 days of storage at 30 ± 5 °C (ST2), 30 ml of vinegar, 9.0 g of NaCl, 18 min of cooking time, and 9.0 g, 19.5 g or 30 g of sugar (the effect of the sugar content at the evaluated range was not significant at 95% confidence level)., Competing Interests: DECLARATION OF CONFLICTING INTERESTSThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The non-stationary nature of the EEG signal poses challenges for the classification of motor imagery. sparse representation classification (SRC) appears as an alternative for classification of untrained conditions and, therefore, useful in motor imagery. Empirical mode decomposition (EMD) deals with signals of this nature and appears at the rear of the classification, supporting the generation of features. In this work we evaluate the combination of these methods in a multiclass classification problem, comparing them with a conventional method in order to determine if their performance is regular. For comparison with SRC we use multilayer perceptron (MLP). We also evaluated a hybrid approach for classification of sparse representations with MLP (RSMLP). For comparison with EMD we used filtering by frequency bands. Feature selection methods were used to select the most significant ones, specifically Random Forest and Particle Swarm Optimization. Finally, we used data augmentation to get a more voluminous base. Regarding the first dataset, we observed that the classifiers that use sparse representation have results equivalent to each other, but they outperform the conventional MLP model. SRC and SRMLP achieve an average accuracy of [Formula: see text] and [Formula: see text] respectively while the MLP is [Formula: see text], representing a gain between [Formula: see text] and [Formula: see text]. The use of EMD in relation to other feature processing techniques is not superior. However, EMD does not influence negatively, there is an opportunity for improvement. Finally, the use of data augmentation proved to be important to obtain relevant results. In the second dataset, we did not observe the same results. Models based on sparse representation (SRC, SRMLP, etc.) have on average a performance close to other conventional models, but without surpassing them. The best sparse models achieve an average accuracy of [Formula: see text] among the subjects in the base, while other model reach [Formula: see text]. The improvement of self-adaptive mechanisms that respond efficiently to the user's context is a good way to achieve improvements in motor imagery applications. However, other scenarios should be investigated, since the advantage of these methods was not proven in all datasets studied. There is still room for improvement, such as optimizing the dictionary of sparse representation in the context of motor imagery. Investing efforts in synthetically increasing the training base has also proved important to reduce the costs of this group of applications., (© 2023. Springer Nature Limited.)

In December 2019, the spread of the SARS-CoV-2 virus to the world gave rise to probably the biggest public health problem in the world: the COVID-19 pandemic. Initially seen only as a disease of the respiratory system, COVID-19 is actually a blood disease with effects on the respiratory tract. Considering its influence on hematological parameters, how does COVID-19 affect cardiac function? Is it possible to support the clinical diagnosis of COVID-19 from the automatic analysis of electrocardiography? In this work, we sought to investigate how COVID-19 affects cardiac function using a machine learning approach to analyze electrocardiography (ECG) signals. We used a public database of ECG signals expressed as photographs of printed signals, obtained in the context of emergency care. This database has signals associated with abnormal heartbeat, myocardial infarction, history of myocardial infarction, COVID-19, and healthy heartbeat. We propose a system to support the diagnosis of COVID-19 based on hybrid deep architectures composed of pre-trained convolutional neural networks for feature extraction and Random Forests for classification. We investigated the LeNet, ResNet, and VGG16 networks. The best results were obtained with the VGG16 and Random Forest network with 100 trees, with attribute selection using particle swarm optimization. The instance size has been reduced from 4096 to 773 attributes. In the validation step, we obtained an accuracy of 94%, kappa index of 0.91, and sensitivity, specificity, and area under the ROC curve of 100%. This work showed that the influence of COVID-19 on cardiac function is quite considerable: COVID-19 did not present confusion with any heart disease, nor with signs of healthy individuals. It is also possible to build a solution to support the clinical diagnosis of COVID-19 in the context of emergency care from a non-invasive and technologically scalable solution, based on hybrid deep learning architectures., (© 2023. International Federation for Medical and Biological Engineering.)

To assess the cortical activity in people with Parkinson's disease (PwP) with different motor phenotype (tremor-dominant-TD and postural instability and gait difficulty-PIGD) and to compare with controls. Twenty-four PwP (during OFF and ON medication) and twelve age-/sex-/handedness-matched healthy controls underwent electrophysiological assessment of spectral ratio analysis through electroencephalography (EEG) at resting state and during the hand movement. We performed a machine learning method with 35 attributes extracted from EEG. To verify the efficiency of the proposed phenotype-based EEG classification the random forest and random tree were tested (performed 30 times, using a tenfolds cross validation in Weka environment). The analyses based on phenotypes indicated a slowing down of cortical activity during OFF medication state in PwP. PD with TD phenotype presented this characteristic at resting and the individuals with PIGD presented during the hand movement. During the ON state, there is no difference between phenotypes at resting nor during the hand movement. PD phenotypes may influence spectral activity measured by EEG. Random forest machine learning provides a slightly more accurate, sensible and specific approach to distinguish different PD phenotypes. The phenotype of PD might be a clinical characteristic that could influence cortical activity., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lima, da Silva, Moreno, Cordeiro da Silva, Musah, Aldosery, Dutra, Ambrizzi, Borges, Tunali, Basibuyuk, Yenigün, Massoni, Browning, Jones, Campos, Kostkova, Silva Filho and dos Santos.)

The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma.

Mosquito surveillance is a crucial process for understanding the population dynamics of mosquitoes, as well as implementing interventional programs for controlling and preventing the spread of mosquito-borne diseases. Environmental surveillance agents who performing routine entomological surveys at properties in areas where mosquito-borne diseases are endemic play a critical role in vector surveillance by searching and destroying mosquito hotspots as well as collate information on locations with increased infestation. Currently, the process of recording information on paper-based forms is time-consuming and painstaking due to manual effort. The introduction of mobile surveillance applications will therefore improve the process of data collection, timely reporting, and field worker performance. Digital-based surveillance is critical in reporting real-time data; indeed, the real-time capture of data with phones could be used for predictive analytical models to predict mosquito population dynamics, enabling early warning detection of hotspots and thus alerting fieldworker agents into immediate action. This paper describes the development of a cross-platform digital system for improving mosquito surveillance in Brazil. It comprises of two components: a dashboard for managers and a mobile application for health agents. The former enables managers to assign properties to health workers who then survey them for mosquitoes and to monitor the progress of inspection visits in real-time. The latter, which is primarily designed as a data collection tool, enables the environmental surveillance agents to act on their assigned tasks of recording the details of the properties at inspections by filling out digital forms built into the mobile application, as well as details relating to mosquito infestation. The system presented in this paper was co-developed with significant input with environmental agents in two Brazilian cities where it is currently being piloted., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Aldosery, Musah, Birjovanu, Moreno, Boscor, Dutra, Santos, Nunes, Oliveira, Ambrizzi, Massoni, dos Santos and Kostkova.)

This paper explores the main factors for mosquito-borne transmission of the Zika virus by focusing on environmental, anthropogenic, and social risks. A literature review was conducted bringing together related information from this genre of research from peer-reviewed publications. It was observed that environmental conditions, especially precipitation, humidity, and temperature, played a role in the transmission. Furthermore, anthropogenic factors including sanitation, urbanization, and environmental pollution promote the transmission by affecting the mosquito density. In addition, socioeconomic factors such as poverty as well as social inequality and low-quality housing have also an impact since these are social factors that limit access to certain facilities or infrastructure which, in turn, promote transmission when absent (e.g., piped water and screened windows). Finally, the paper presents short-, mid-, and long-term preventative solutions together with future perspectives. This is the first review exploring the effects of anthropogenic aspects on Zika transmission with a special emphasis in Brazil., (© 2021. The Author(s).)

The Covid-19 pandemic, a disease transmitted by the SARS-CoV-2 virus, has already caused the infection of more than 120 million people, of which 70 million have been recovered, while 3 million people have died. The high speed of infection has led to the rapid depletion of public health resources in most countries. RT-PCR is Covid-19's reference diagnostic method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach and represented by co-occurrence matrices. This technique eliminates multiple sequence alignment. Through the proposed method, it is possible to identify virus sequences from a large database: 347,363 virus DNA sequences from 24 virus families and SARS-CoV-2. When comparing SARS-CoV-2 with virus families with similar symptoms, we obtained [Formula: see text] for sensitivity and [Formula: see text] for specificity with MLP classifier and 30% overlap. When SARS-CoV-2 is compared to other coronaviruses and healthy human DNA sequences, we obtained [Formula: see text] for sensitivity and [Formula: see text] for specificity with MLP and 50% overlap. Therefore, the molecular diagnosis of Covid-19 can be optimized by combining RT-PCR and our pseudo-convolutional method to identify DNA sequences for SARS-CoV-2 with greater specificity and sensitivity.

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post-the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 da Silva, de Lima, da Silva, Silva, Marques, de Araújo, Albuquerque Júnior, de Souza, de Santana, Gomes, Barbosa, Musah, Kostkova, dos Santos and da Silva Filho.)

Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil. Methods: We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented. Results: The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%. Conclusion: The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a cost-effective, concise, and robust manner. This novel tools can play an important role for guiding the course of action against the Covid-19 pandemic for Brazil and country neighbors in South America., (Copyright © 2020 de Lima, da Silva, da Silva, Luiz Silva, Marques, de Araújo, Albuquerque Júnior, de Souza, de Santana, Gomes, de Freitas Barbosa, Musah, Kostkova, dos Santos and da Silva Filho.)

The multimodal approach for patients with head and neck cancer (HNC) includes treatment with chemoradiation therapy (CRT). A common concern regarding CRT side effects is the occurrence of structural and physiological alterations of the salivary glands due to exposure to ionizing radiation. The aim of this study is to examine the morphology, volume, and density of the parotid glands before and after CRT in HNC patients. A total of 49 HNC patients treated exclusively with CRT were included in the study. Ninety-eight parotid glands were evaluated before and after treatment by using contrast-enhanced computed tomography (CECT). Shapiro-Wilk test was performed, and the variables (pre-CRT and post-CRT) presented normal distribution. Pearson's coefficient was used to assess the correlation between volume and density. CRT resulted in a significant decrease in the mean volume of the parotid glands (i.e., original volume reduced by 20.5%; P < 0.0001). CRT induced a 30.0% (7 Hounsfield units) increase in density of the right parotid gland and a 24.9% (8 Hounsfield units) increase in density of the left parotid gland ( P =0.0198 and P =0.0079, respectively). Changes in morphology and spatial configuration, increased density, and substantial loss of volume of the parotid glands were observed after CRT. There was also a difference in density ( P =0.003) in the right-side parotid glands in comparison between xerostomic and nonxerostomic groups of patients. These facts lead to the need for a personalized CRT planning in order to minimize oral complications related to the treatment., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Wellington Pereira dos Santos et al.)

Objective: To investigate the perception of male users of a home care service ( Programa Melhor em Casa , PMC) provided by the Unified Health System (SUS) in Brazil regarding the fulfilment of their health needs., Method: The present observational and descriptive study, with qualitative design, was performed with men registered with PMC in Cuité, a municipality located in the state of Paraíba, Northeastern Brazil. Ten of 19 men registered with PMC in Cuité were interviewed. Data collection was based on a semi-structured interview with participants. Results were analyzed using Bardin's content analysis of thematic categories., Results: Three thematic categories were identified: knowledge of participants regarding the health problem for which they were receiving home care; masculine perception regarding the health needs in the home; and perception of participants regarding the advantages of home care. The results showed that the men interviewed were unaware of many important aspects of their health condition, such as the reason for their inclusion in a home care program. Regarding the advantages of PMC, participants mentioned the quick response by health care workers, the avoidance of long waiting lines that are usual in public health care services, and the comfort of the home., Conclusions: The acceptance and satisfaction of users with PMC was remarkable. However, a high level of unawareness regarding the participants' own health needs was observed. Additional studies are required to broaden the discussion regarding home care., Competing Interests: Conflitos de interesse. Nada declarado pelos autores.

Background and Objective: According to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic development, among developing and underdevelopment countries mortality rates are still high due to low availability of early detection technologies. From the clinical point of view, mammography is still the most effective diagnostic technology, given the wide diffusion of the use and interpretation of these images., Methods: Herein this work we propose a method to detect and classify mammographic lesions using the regions of interest of images. Our proposal consists in decomposing each image using multi-resolution wavelets. Zernike moments are extracted from each wavelet component. Using this approach, we can combine both texture and shape features, which can be applied both to the detection and classification of mammary lesions. We used 355 images of fatty breast tissue of IRMA database, with 233 normal instances (no lesion), 72 benign, and 83 malignant cases., Results: Classification was performed by using SVM and ELM networks with modified kernels in order to optimize accuracy rates, reaching 94.11%. Considering both accuracy rates and training times, we defined the ration between average percentage accuracy and average training time in a reverse order. Our proposal was 50 times higher than the ratio obtained using state-of-the-art approaches., Conclusions: As our proposed model can combine high accuracy rate with low learning time, whenever a new data is received, our work will be able to save a lot of time, hours, in learning process in relation to the best method of the state-of-the-art., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

The present paper describes a novel, simple and reliable differential pulse voltammetric method for determining amitriptyline (AMT) in pharmaceutical formulations. It has been described for many authors that this antidepressant is electrochemically inactive at carbon electrodes. However, the procedure proposed herein consisted in electrochemically oxidizing AMT at an unmodified carbon nanotube paste electrode in the presence of 0.1 mol L(-1) sulfuric acid used as electrolyte. At such concentration, the acid facilitated the AMT electroxidation through one-electron transfer at 1.33 V vs. Ag/AgCl, as observed by the augmentation of peak current. Concerning optimized conditions (modulation time 5 ms, scan rate 90 mV s(-1), and pulse amplitude 120 mV) a linear calibration curve was constructed in the range of 0.0-30.0 μmol L(-1), with a correlation coefficient of 0.9991 and a limit of detection of 1.61 μmol L(-1). The procedure was successfully validated for intra- and inter-day precision and accuracy. Moreover, its feasibility was assessed through analysis of commercial pharmaceutical formulations and it has been compared to the UV-vis spectrophotometric method used as standard analytical technique recommended by the Brazilian Pharmacopoeia., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Objective: To compare the diaphragmatic mobility of healthy subjects during incentive spirometry with a volume-oriented device, during incentive spirometry with a flow-oriented device, and during diaphragmatic breathing. To compare men and women in terms of diaphragmatic mobility during these three types of breathing exercises., Methods: We evaluated the pulmonary function and diaphragmatic mobility of 17 adult healthy volunteers (9 women and 8 men). Diaphragmatic mobility was measured via ultrasound during diaphragmatic breathing and during the use of the two types of incentive spirometers., Results: Diaphragmatic mobility was significantly greater during the use of the volume-oriented incentive spirometer than during the use of the flow-oriented incentive spirometer (70.16 ± 12.83 mm vs. 63.66 ± 10.82 mm; p = 0.02). Diaphragmatic breathing led to a greater diaphragmatic mobility than did the use of the flow-oriented incentive spirometer (69.62 ± 11.83 mm vs. 63.66 ± 10.82 mm; p = 0.02). During all three types of breathing exercises, the women showed a higher mobility/FVC ratio than did the men., Conclusions: Incentive spirometry with a volume-oriented device and diaphragmatic breathing promoted greater diaphragmatic mobility than did incentive spirometry with a flow-oriented device. Women performed better on the three types of breathing exercises than did men.

About 8% of men are affected by color blindness. That population is at a disadvantage since they cannot perceive a substantial amount of the visual information. This work presents two computational tools developed to assist color blind people. The first one tests color blindness and assess its severity. The second tool is based on Fuzzy Logic, and implements a method proposed to simulate real red and green color blindness in order to generate synthetic cases of color vision disturbance in a statistically significant amount. Our purpose is to develop correction tools and obtain a deeper understanding of the accessibility problems faced by people with chromatic visual impairment.

Objective: To determine whether COPD patients with diaphragmatic dysfunction present higher risk of mortality than do those without such dysfunction., Methods: We evaluated pulmonary function, diaphragm mobility and quality of life, as well as determining the Body mass index, airway Obstruction, Dyspnea, and Exercise capacity (BODE) index, in 42 COPD patients. The patients were allocated to two groups according to the degree to which diaphragm mobility was impaired: low mobility (LM; mobility < 33.99 mm); and high mobility (HM; mobility > 34.00 mm). The BODE index and the quality of life were quantified in both groups. All patients were followed up prospectively for up to 48 months in order to determine the number of deaths resulting from respiratory complications due to COPD., Results: Of the 42 patients evaluated, 20 were allocated to the LM group, and 22 were allocated to the HM group. There were no significant differences between the groups regarding age, lung hyperinflation or quality of life. However, BODE index values were higher in the LM group than in the HM group (p = 0.01). During the 48-month follow-up period, there were four deaths within the population studied, and all of those deaths occurred in the LM group (15.79%; p = 0.02)., Conclusions: These findings suggest that COPD patients with diaphragmatic dysfunction, characterized by low diaphragm mobility, have a higher risk of death than do those without such dysfunction.

Biology, Psychology and Social Sciences are intrinsically connected to the very roots of the development of algorithms and methods in Computational Intelligence, as it is easily seen in approaches like genetic algorithms, evolutionary programming and particle swarm optimization. In this work we propose a new optimization method based on dialectics using fuzzy membership functions to model the influence of interactions between integrating poles in the status of each pole. Poles are the basic units composing dialectical systems. In order to validate our proposal we designed a segmentation method based on the optimization of k-means using dialectics for the segmentation of MR images. As a case study we used 181 MR synthetic multispectral images composed by proton density, T(1)- and T(2)-weighted synthetic brain images of 181 slices with 1 mm, resolution of 1 mm(3), for a normal brain and a noiseless MR tomographic system without field inhomogeneities, amounting a total of 543 images, generated by the simulator BrainWeb [2]. Our principal target here is comparing our proposal to k-means, fuzzy c-means, and Kohonen's self-organized maps, concerning the quantization error, we proved that our method can improved results obtained using k-means.

Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted (DW) magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on Objective Dialectical Classifiers, a new method based on Dialectics as defined in the Philosophy of Praxis. A 2-degree polynomial network with supervised training is used to generate the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.

The Aedes Aegypti mosquito is the vector of the most difficult public health problems in tropical and semi-tropical world: the epidemic proliferation of dengue, a viral disease that can cause human beings death specially in its most dangerous form, dengue haemorrhagic fever. One of the most useful methods for mosquito detection and surveillance is the ovitraps: special traps to collect eggs of the mosquito. It is very important to count the number of Aedes Aegypti eggs present in ovitraps. This counting is usually performed in a manual, visual and non-automatic form. This work approaches the development of automatic methods to count the number of eggs in ovitraps images using image processing, particularly color segmentation and mathematical morphology-based non-linear filters.

Objective: To assess, using ultrasound, the effects that changes in body position have on diaphragmatic mobility in healthy subjects during spontaneous breathing., Methods: The study involved seven healthy female volunteers, all of whom were nonsmokers, well nourished, and free of any cardiopulmonary disease. They were submitted to pulmonary function testing and ultrasound evaluation of the mobility of the right diaphragm by the craniocaudal displacement of the left branch of the portal vein using an ultrasound device in mode B. The mobility of the right diaphragm was evaluated in right decubitus and in left decubitus. The order of evaluation was previously determined in a random drawing., Results: The average mobility of the right diaphragm in right decubitus (51.30 +/- 9.69 mm) was significantly higher (p = 0.03) than that observed in left decubitus (45.93 +/- 10.37 mm)., Conclusion: The results suggest that, during spontaneous ventilation, the dependent portion of the diaphragm presents greater mobility than does the nondependent portion, and that the technique used was sufficiently sensitive to detect variations in diaphragmatic mobility related to changes in posture.

Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on multilayer perceptrons and Kohonen Self-Organized Map classifiers. We assume the classes of interest can be separated by hyperquadrics. Therefore, a 2-degree polynomial network is used to classify the original image, generating the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.

In the present paper a focused-microwave Kjeldahl digestion procedure without metal catalyst for nitrogen determination in bean samples was developed. Temperature at which the decomposition plateau occurs, mass of potassium sulphate and either volume of sulphuric acid or hydrogen peroxide were optimised. Results of the two-level full factorial design (2(4)) based on an analysis of variance demonstrated that only the decomposition plateau temperature and the sulphuric acid volume were statistically significant. Optimal conditions for the digestion of bean samples were obtained by using Doehlert design. The modified digestion procedure of 0.25g of bean samples has been performed in 27min at optimised conditions. The accuracy of the developed procedure by the analysis of the two certified reference materials, peach leaves (NIST 1547) and apple leaves (NIST 1515). The t-test applied to the results revealed that they are in agreement (p > 0.05) with the certified values. The precision, expressed as relative standard deviation (R.S.D.) was of 0.96% for four successive Kjeldahl nitrogen determinations. In addition, interlaboratory exercises were performed with several bean samples in reference Brazilian food control laboratory.

The stratum corneum (SC) protein dynamics in the sulfhydryl group regions was studied by electron paramagnetic resonance (EPR) spectroscopy of a covalently attached maleimide derivative spin label. A two-state model for the nitroxide described the coexistence of two spectral components in the EPR spectra. The so-called strongly immobilized component arises from a spin-label fraction with the nitroxide moiety hydrogen-bonded to protein (rigid structure) and the weakly immobilized component is provided by the spin labels with higher mobility (approximately 10 times greater) exposed to the aqueous environment. The relative populations between these two states are in thermodynamic equilibrium. The apparent energetic gain for the nitroxide to form a hydrogen bond with the backbone rather than to be dissolved in the local environment was approximately 10 kcal/mol in the temperature range of 2-30 degrees C and approximately 6 kcal/mol in the range of 30-70 degrees C. Urea treatment caused a drastic increase in the segmental motion of the polypeptide chains that was completely reversible by its removal. Our analyses also indicated that the urea induced unfolding of the SC proteins opening the thiol group cavities. This work can also be useful to improve the spectral analysis of site-directed spin-labeling, especially for a more quantitative description of the nitroxide side chain mobility.