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Improved management of livestock in resource-limited settings can provide a means towards improved human nutrition and livelihoods. However, gastrointestinal nematodes (GIN) are a significant production-limiting factor. Anthelmintics play a role in GIN management; however, few anthelmintic classes are available in many low-middle-income countries. Utilising a limited range of classes may increase selection for anthelmintic resistance; therefore, strategies to reduce other selective pressures are of heightened importance. Avoiding anthelmintic underdosing is one such strategy, but it can be challenging without access to accurate bodyweight measurement. Many previous studies have used thoracic girth as a practical proxy for bodyweight in goats; however, they have rarely considered the potential impact of natural variation on therapeutic doses. Here, the relationship between bodyweight and thoracic girth was modelled using data from 820 goats from three Malawian biomes in two seasons, with the specific aim of avoiding underestimation of bodyweight. The internally cross-validated linear regression (∛Weight ~ 0.053 + 0.040*Girth, R 2  = 0.92, rounded up to the nearest 5 kg) was validated against data from an additional 352 Malawian goats (1.4% of goats allocated an underdose and 10.2% allocated a dose > 200% of bodyweight). The equation was further externally validated using an historical dataset of 150 goats from Assam, India (2.7% of goats were allocated to an underdose and 24.8% allocated to a > 200% of bodyweight). These results suggest that a more globally generalisable approach may be feasible, provided the accuracy of the estimate is considered alongside the therapeutic index of the pharmaceutical., (© 2023. The Author(s).)

Hypsarrhythmia is a specific chaotic morphology, present in the interictal period of the electroencephalogram (EEG) signal in patients with West Syndrome (WS), a severe form of childhood epilepsy and that, recently, was also identified in the examinations of patients with Zika Virus Congenital Syndrome (ZVCS). This innovative work proposes the development of a computational methodology for analysis and differentiation, based on the time-frequency domain, between the chaotic pattern of WS and ZVCS hypsarrhythmia. The EEG signal time-frequency analysis is carried out from the Continuous Wavelet Transform (CWT). Four joint moments-joint mean-[Formula: see text], joint variance-[Formula: see text], joint skewness-[Formula: see text], and joint kurtosis-[Formula: see text]-and four entropy measurements-Shannon, Log Energy, Norm, and Sure-are obtained from the CWT to compose the representative feature vector of the EEG hypsarrhythmic signals under analysis. The performance of eight classical types of machine learning algorithms are verified in classification using the k-fold cross validation and leave-one-patient-out cross validation methods. Discrimination results provided 78.08% accuracy, 85.55% sensitivity, 73.21% specificity, and AUC = 0.89 for the ANN classifier., (© 2022. The Author(s).)

Non-standard diesel blends can be harmful to the environment and human health. In this context, a simple analytical method to estimate the biodiesel mixture ratio in diesel was developed based on impedance spectroscopy (IS) associated with interdigitated sensors. In this article, four different interdigitated sensors with varied comb spacing (G) were simulated using the COMSOL Multiphysics software. Based on finite element simulations, four interdigitated electrode architectures were manufactured and evaluated. The best geometry was chosen according to theoretical data simulations, and its interdigitated electrodes were manufactured for the compositional evaluation of pseudo-binary biodiesel-diesel mixtures. According to the X-ray powder diffraction technique, the deposition of the conductive layer (Au 0 ) over the surface of the dielectric substrate (SiO 2 ) did not alter its phase composition. In the analysis of AFM and SEM, it was possible to observe irregular edges on the electrodes, possibly related to the manufacturing process of the thin layers and mechanical stability. Another characteristic observed in the AFM images was the height of the step of the gold layer of the sensor. Several cross sections were obtained, and the mean step value was 225.71 ± 0.0032 nm. Although there were differences in the roughness, the whole sensor had nanometric roughness. Based on the finite element method simulation performed, it can be assumed that the geometric parameters more suitable for the manufacturing of the electrode are W = 20 µm, L = 1000 µm, G = 50 µm, and N = 40 digits. The electrical characterization performed by impedance spectroscopy showed that we could differentiate between biodiesel and diesel fuels and their pseudo-binary mixtures in the low-frequency region.

In this paper, a bioinspired method in the magnetic field memory of the bees, applied in a rover of precision pollination, is presented. The method calculates sharpness features by entropy and variance of the Laplacian of images segmented by color in the HSV system in real-time. A complementary positioning method based on area feature extraction between active markers was developed, analyzing color characteristics, noise, and vibrations of the probe in time and frequency, through the lateral image of the probe. From the observed results, it can be seen that the unsupervised method does not require previous calibration of target dimensions, histogram, and distances involved in positioning. The algorithm showed less sensitivity in the extraction of sharpness characteristics regarding the number of edges and greater sensitivity to the gradient, allowing unforeseen operation scenarios, even in small sharpness variations, and robust response to variance local, temporal, and geophysical of the magnetic declination, not needing luminosity after scanning, with the two freedom of degrees of the rotation.

We evaluate the genetic characterization of 132 HIV-1 pol sequences from children and adolescents undergoing antiretroviral therapy in Northeast Brazil. Phylogenetic and recombination analyses were performed using the maximum likelihood method using SeaView version 4 and SIMPLOT software. Most individuals harbored HIV-1 B (84.8%) and BF recombinants (9.8%), although other non-B subtypes were detected: HIV-1 C (1.5%), HIV-1 F (2.4%), and BC recombinants (1.5%). Antiretroviral resistance was 47% (95% confidence interval [CI]: 38.7%-55.4%). Non-nucleoside reverse transcriptase inhibitors (NNRTIs) showed higher frequencies of primary mutations, with 40.9% (95% CI: 32.9%-49.4%), followed by nucleoside reverse transcriptase inhibitors (NRTI) and protease inhibitors (PIs) with 34.8% (95% CI: 27.3-43.3) and 6.1% (95% CI: 3.1%-11.5%), respectively. Among NRTIs, higher resistance levels were observed for abacavir, emtricitabine, and lamivudine; for NNRTI, nevirapine and efavirenz. The most common primary mutations found were M184V (29.5%), K103N (25%), M41L (9.8%), T215Y (8.3%), and G190A (8.3%). Our findings highlight the importance of surveillance of resistance mutations, which contributes to the continuous updating and implementation of preventive measures to decrease mother-to-child-transmission and transmitted drug resistance., (© 2021 Wiley Periodicals LLC.)

Studies using zebrafish (Danio rerio) in neuro-behavioural research are growing. Measuring fish behavior by computational methods is one of the most efficient ways to avoid human bias in experimental analyses, extending them to various approaches. Sometimes, thorough analyses are difficult to do, as fish can behave unpredictably during an experimental strategy. However, the analyses can be implemented in an automated way, using an online strategy and video processing for a complete assessment of the zebrafish behavior, based on the detection and tracking of fish during an activity. Here, a fully automatic conditioning and detailed analysis of zebrafish behavior is presented. Microcontrolled components were used to control the delivery of visual and sound stimuli, in addition to the concise amounts of food after conditioned stimuli for adult zebrafish groups in a conventional tank. The images were captured and processed for automatic detection of the fish, and the training of the fish was done in two evaluation strategies: simple and complex. In simple conditioning, the zebrafish showed significant responses from the second attempt, learning that the conditioned stimulus was a predictor of food presentation in a specific space of the tank, where the food was dumped. When the fish were subjected to two stimuli for decision-making in the food reward, the zebrafish obtained better responses to red light stimuli in relation to vibration. The behavior change was clear in stimulated fish in relation to the control group, thus, the distances traveled and the speed were greater, while the polarization was lower in stimulated fish. This automated system allows for the conditioning and assessment of zebrafish behavior online, with greater stability in experiments, and in the analysis of the behavior of individual fish or fish schools, including learning and memory studies.

Fish show rapid movements in various behavioral activities or associated with the presence of food. However, in periods of rapid movement, the rate at which occlusion occurs among the fish is quite high, causing inconsistency in the detection and tracking of fish, hindering the fish's identity and behavioral trajectory over a long period of time. Although some algorithms have been proposed to solve these problems, most of their applications were made in groups of fish that swim in shallow water and calm behavior, with few sudden movements. To solve these problems, a convolutional network of object recognition, YOLOv2, was used to delimit the region of the fish heads to optimize individual fish detection. In the tracking phase, the Kalman filter was used to estimate the best state of the fish's head position in each frame and, subsequently, the trajectories of each fish were connected among the frames. The results of the algorithm show adequate performances in the trajectories of groups of zebrafish that exhibited rapid movements.

This paper intends to classify the interictal state with hypsarrhythmia in patients with Zika Virus Congenital Syndrome (ZVCS) and of the ictal state in patients with epilepsy in childhood without the presence of hypsarrhythmia. Hypsarrhythmia is a specific interictal chaotic morphology, and the correct distinction between these two EEG states is crucial to improving the cognitive development of these epileptic patients. The proposed approach was assessed using the proprietary database of Casa Ninar, which contains data regarding children from northeastern Brazil born with microcephaly caused by the Zika virus. We also used data from the CHB-MIT database. Fundamental rhythms of the EEG signal δ, θ, α, and β were analyzed, and then decomposed by Discrete Wavelet Transform, in which 45 mother wavelet functions were tested to determine the most appropriate function to represent the EEG signals in the hypsarrhythmia interictal and ictal states. We extracted Shannon, Log Energy, Norm, and Sure entropy measures of the subbands as relevant features, and the combinations among them were applied in the state-of-the-art machine learning methods. The combination of Sure entropy with Shannon entropy, or with Log Energy and Norm, extracted from the δ rhythm, allowed for the best linear separability between the classes in most of the classifiers, obtaining 100% accuracy, sensitivity, and specificity., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Intrauterine infection with the Zika virus (ZIKV) has been connected to severe brain malformations, microcephaly, and abnormal electrophysiological activity., Methods: We describe the interictal electroencephalographic (EEG) recordings of 47 children born with ZIKV-derived microcephaly. EEGs were recorded in the first year of life and correlated with brain morphology. In 31 subjects, we tested the association between computed tomography (CT) findings and interictal epileptiform discharges (IED). In eighteen, CTs were used for correlating volumetric measurements of the brainstem, cerebellum, and prosencephalon with the rate of IED., Findings: Twenty-nine out of 47 (62%) subjects were diagnosed as having epilepsy. Those subjects presented epileptiform discharges, including unilateral interictal spikes (26/29, 90%), bilateral synchronous and asynchronous interictal spikes (21/29, 72%), and hypsarrhythmia (12/29, 41%). Interestingly, 58% of subjects with clinical epilepsy were born with rhombencephalon malformations, while none of the subjects without epilepsy showed macroscopic abnormalities in this region. The presence of rhombencephalon malformation was associated with epilepsy (odds ratio of 34; 95% CI: 2 - 654). Also, the presence of IED was associated with smaller brain volumes. Age-corrected total brain volume was inversely correlated with the rate of IED during sleep. Finally, 11 of 44 (25%) subjects presented sleep spindles. We observed an odds ratio of 0·25 (95% CI: 0·06 - 1·04) for having sleep spindles given the IED presence., Interpretation: The findings suggest that certain CT imaging features are associated with an increased likelihood of developing epilepsy, including higher rates of IED and impaired development of sleep spindles, in the first year of life of CZVS subjects., Funding: This work was supported by the Brazilian Federal Government through a postdoctoral fellowship for EBS (Talented Youth, Science without Borders), an undergraduate scholarship for AJR (Institutional Program of Science Initiation Scholarships, Federal University of Rio Grande do Norte, Brazil), by International Centre for Genetic Engineering and Biotechnology (CRP/BRA18-05_EC) and by CAPES (Grant number 440893/2016-0), and CNPq (Grant number 88881.130729/2016-01)., Competing Interests: We declare no competing interests associated with this work., (© 2020 The Authors.)

As countries turn wealthier, some health indicators, such as child mortality, seem to have well-defined trends. However, others, including cardiovascular conditions, do not follow clear linear patterns of change with economic development. Abnormal blood pressure is a serious health risk factor with consequences for population growth and longevity as well as public and private expenditure in health care and labor productivity. This also increases the risk of the population in certain pandemics, such as COVID-19. To determine the correlation of income and blood pressure, we analyzed time-series for the mean systolic blood pressure (SBP) of men's population (mmHg) and nominal Gross Domestic Product per capita (GDPPC) for 136 countries from 1980 to 2008 using regression and statistical analysis by Pearson's correlation (r). Our study finds a trend similar to an inverted-U shaped curve, or a 'Heart Kuznets Curve'. There is a positive correlation (increase GDPPC, increase SBP) in low-income countries, and a negative correlation in high-income countries (increase GDPPC, decrease SBP). As country income rises people tend to change their diets and habits and have better access to health services and education, which affects blood pressure. However, the latter two may not offset the rise in blood pressure until countries reach a certain income. Investing early in health education and preventive health care could avoid the sharp increase in blood pressure as countries develop, and therefore, avoiding the 'Heart Kuznets Curve' and its economic and human impacts., (© 2020 Elsevier Ltd. All rights reserved.)

The HIV-1 epidemic in Brazil has been growing in northeast and north regions, particularly an increase in AIDS cases among the younger male population has been observed. This study aims to characterize the HIV-1 genetic diversity and to evaluate its antiretroviral resistance profile among individuals presenting virological failure in the state of Maranhão-Brazil. HIV-1 pol gene sequences from 633 patients on antiretroviral therapy were obtained from the Department of Surveillance, Prevention and Control of Sexually Transmitted Infections, HIV/AIDS and Viral Hepatitis of the Brazilian Ministry of Health. Phylogenetic and recombination analyses were performed to characterize viral genetic diversity. The presence of antiretroviral resistance mutations was assessed using the HIV Drug Resistance Database online platform of Stanford University. A predominance of subtype B (84.5%) was observed, followed by recombinant BF (9.5%), where more than half of the sequences were dispersed in 3 clusters. Antiretroviral resistance was detected in 74.1% of the sequences, and it was significantly higher for nucleoside analogue reverse-transcriptase inhibitors (NRTIs) than for non-nucleoside analogue reverse-transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs). Inference of putative transmissions clusters identified 11 clusters with 22 query sequences (22/633, 3.5%). Thus, we conclude that continuous monitoring of the molecular epidemiology of HIV-1 is essential for prevention strategies, epidemic control, and treatment adequacy., Competing Interests: The authors have declared that no competing interests exist.

Background: Excess body fat has been growing alarmingly among adolescents, especially in low income and middle income countries where access to health services is scarce. Currently, the main method for assessing overweight in adolescents is the body mass index, but its use is criticized for its low sensitivity and high specificity, which may lead to a late diagnosis of comorbidities associated with excess body fat, such as cardiovascular diseases. Thus, the aim of this study was to develop a computational model using linear regression to predict obesity in adolescents and compare it with commonly used anthropometric methods. To improve the performance of our model, we estimated the percentage of fat and then classified the nutritional status of these adolescents., Methods: The model was developed using easily measurable socio-demographic and clinical variables from a database of 772 adolescents of both genders, aged 10-19 years. The predictive performance was evaluated by the following metrics: accuracy, sensitivity, specificity, and area under ROC curve. The performance of the method was compared to the anthropometric parameters: body mass index and waist-to-height ratio., Results: Our model showed a high correlation (R = 0.80) with the body fat percentage value obtained through bioimpedance. In addition, regarding discrimination, our model obtained better results compared to BMI and WHtR: AUROC = 0.80, 0.64, and 0.55, respectively. It also presented a high sensitivity of 92% and low false negative rate (6%), while BMI and WHtR showed low sensitivity (27% and 9.9%) and a high false negative rate (65% and 53%), respectively., Conclusions: The computational model of this study obtained a better performance in the evaluation of excess body fat in adolescents, compared to the usual anthropometric indicators presenting itself as a low cost alternative for screening obesity in adolescents living in Brazilian regions where financial resources are scarce.

Hypsarrhythmia is an electroencephalographic pattern specific to some epileptic syndromes that affect children under one year of age. The identification of this pattern, in some cases, causes disagreements between experts, which is worrisome since an inaccurate diagnosis can bring complications to the infant. Despite the difficulties in visually identifying hypsarrhythmia, options of computerized assistance are scarce. Aiming to collaborate with the recognition of this electropathological pattern, we propose in this paper a mathematical index that can help electroencephalography experts to identify hypsarrhythmia. We performed hypothesis tests that indicated significant differences in the groups under analysis, where the p -values were found to be extremely small.

Background: The electrocardiogram (ECG) is one of the most non-invasive techniques to give support to the atrial fibrillation (AF) diagnosis. Several authors use the temporal difference between two consecutive R waves, a method known as RR interval, to perform the AF diagnosis. However, RR interval-based analysis does not detect distortions on the other ECG waves., Purpose: Thus, the present work proposes a diagnostic decision support systems for AF based on higher order spectrum analysis of the voltage variation on the ECG.., Methods: The proposed method was used aiming AF classifying. The classifier is composed by two screening stages: one based on the average and another on the average deviation of kurtosis of the ECG signals. Heartbeat obtained from the MIT-BIH atrial fibrillation and MIT-BIH normal were used., Results: ECG signal featured by kurtosis outperforms second order statistics based metrics in up to 476 times, and up to 110 times above the RR interval. The screening methods obtained sensitivity equal to 100% and specificity is up to 84.04%. The two screening methods combined provided an AF classifier with an accuracy rate at diagnosis of 100%. The results presented take into account windows of up to five heartbeats and a 99.73% confidence interval., Conclusion: The results obtained by the proposed method can be used to support decision-making in clinical practices with a diagnostic accuracy rate of 90.04% to 100%., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Breast cancer is the second type of cancer that most affects women in the world, losing only for non-melanoma skin cancer. Breast density can hinder the location of masses, especially in early stages. In this work, the use of independent component analysis for detecting lesions in dense breasts is proposed. Several works suggest the use of computer aided diagnosis (CAD), increasing sensitivity to over 90% in detecting cancer in nondense breasts, however there are few published studies about detecting in dense breasts. To analyse its efficiency in relation to other segmentation techniques, we compare the performance with principal component analysis. To measure the quality of the segmentation obtained by the two methods, an area overlay measure will be used. To verify if there was any difference between the results of the proposed methods in the detection of lesions in nondense breasts and in dense breasts, a statistic test for two proportions was used. Experimental results on the Mini-MIAS and DDSM database showed an accuracy of 92.71% in detecting masses in nondense and 79.17% in dense breasts. All experiments showed that the ICA filters have a better performance for detect lesions in dense breast, compared with PCA. Contrary to previous works, our experiments showed that there is actually a significant difference between the detection of masses in dense and nondense breasts. This study can help specialist to detect lesions in dense breast., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background and Objectives: Neuraxial anesthesia (NA) has been used in association with general anesthesia (GA) for coronary artery bypass; however, anticoagulation during surgery makes us question the viability of benefits by the risk of epidural hematoma. The aim of this study was to perform a meta-analyzes examining the efficacy of NA associated with GA compared to GA alone for coronary artery bypass on mortality reduction., Methods: Mortality, arrhythmias, cerebrovascular accident (CVA), myocardial infarction (MI), length of hospital stay (LHS), length of ICU stay (ICUS), reoperations, blood transfusion (BT), quality of life, satisfaction degree, and postoperative cognitive dysfunction were analyzed. The weighted mean difference (MD) was estimated for continuous variables, and relative risk (RR) and risk difference (RD) for categorical variables., Results: 17 original articles analyzed. Meta-analysis of mortality (RD=-0.01, 95% CI=-0.03 to 0.01), CVA (RR=0.79, 95% CI=0.32-1.95), MI (RR=0.96, 95% CI=0.52-1.79) and LHS (MD=-1.94, 95% CI=-3.99 to 0.12) were not statistically significant. Arrhythmia was less frequent with NA (RR=0.68, 95% CI=0.50-0.93). ICUS was lower in NA (MD=-2.09, 95% CI=-2.92 to -1.26)., Conclusion: There was no significant difference in mortality. Combined NA and GA showed lower incidence of arrhythmias and lower ICUS., (Copyright © 2014 Sociedade Brasileira de Anestesiologia. Published by Elsevier Editora Ltda. All rights reserved.)

Background and Objectives: Neuraxial anesthesia (NA) has been used in association with general anesthesia (GA) for coronary artery bypass; however, anticoagulation during surgery makes us question the viability of benefits by the risk of epidural hematoma. The aim of this study was to perform a meta-analyzes examining the efficacy of NA associated with GA compared to GA alone for coronary artery bypass on mortality reduction., Methods: Mortality, arrhythmias, cerebrovascular accident (CVA), myocardial infarction (MI), length of hospital stay (LHS), length of ICU stay (ICUS), reoperations, blood transfusion (BT), quality of life, satisfaction degree, and postoperative cognitive dysfunction were analyzed. The weighted mean difference (MD) was estimated for continuous variables, and relative risk (RR) and risk difference (RD) for categorical variables., Results: 17 original articles analyzed. Meta-analysis of mortality (RD=-0.01, 95% CI=-0.03 to 0.01), CVA (RR=0.79, 95% CI=0.32 to 1.95), MI (RR=0.96, 95% CI=0.52 to 1.79) and LHS (MD=-1.94, 95% CI=-3.99 to 0.12) were not statistically significant. Arrhythmia was less frequent with NA (RR=0.68, 95% CI=0.50 to 0.93). ICUS was lower in NA (MD=-2.09, 95% CI=-2.92 to -1.26)., Conclusion: There was no significant difference in mortality. Combined NA and GA showed lower incidence of arrhythmias and lower ICUS., (Copyright © 2014 Sociedade Brasileira de Anestesiologia. Publicado por Elsevier Editora Ltda. All rights reserved.)

Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k -nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods., Competing Interests: The authors declare that they have no competing interests.

Background and Objectives: Recently, administration of sciatic nerve block has been revised due to the potential benefit for postoperative analgesia and patient satisfaction after the advent of ultrasound. The aim of this study was to describe the anatomical relations of the sciatic nerve in the popliteal fossa to determine the optimal distance the needle must be positioned in order to realize the sciatic nerve block anterior to its bifurcation into the tibial and common fibular nerve., Method: The study was conducted by dissection of human cadavers' popliteal fossa, fixed in 10% formalin, from the Laboratory of Human Anatomy and Morphology Departments of the Universidade Federal de Alagoas and Universidade de Ciências da Saúde de Alagoas. Access to the sciatic nerve was obtained., Results: 44 popliteal fossa were analyzed. The bifurcation of the sciatic nerve in relation to the apex of the fossa was observed. There was bifurcation in: 67.96% below the apex, 15.90% above the apex, 11.36% near the apex, and 4.78% in the gluteal region., Conclusions: The sciatic nerve bifurcation to its branches occurs at various levels, and the chance to succeed when the needle is placed between 5 and 7 cm above the popliteal is 95.22%., (Copyright © 2014 Sociedade Brasileira de Anestesiologia. Published by Elsevier Editora Ltda. All rights reserved.)

Background and Objectives: Recently, administration of sciatic nerve block has been revised due to the potential benefit for postoperative analgesia and patient satisfaction after the advent of ultrasound. The aim of this study was to describe the anatomical relations of the sciatic nerve in the popliteal fossa to determine the optimal distance the needle must be positioned in order to realize the sciatic nerve block anterior to its bifurcation into the tibial and common fibular nerve., Method: The study was conducted by dissection of human cadavers' popliteal fossa, fixed in 10% formalin, from the Laboratory of Human Anatomy and Morphology Departments of the Universidade Federal de Alagoas and Universidade de Ciências da Saúde de Alagoas. Access to the sciatic nerve was obtained., Results: 44 popliteal fossa were analyzed. The bifurcation of the sciatic nerve in relation to the apex of the fossa was observed. There was bifurcation in: 67.96% below the apex, 15.90% above the apex, 11.36% near the apex, and 4.78% in the gluteal region., Conclusions: The sciatic nerve bifurcation to its branches occurs at various levels, and the chance to succeed when the needle is placed between 5 and 7 cm above the popliteal is 95.22%., (Copyright © 2014 Sociedade Brasileira de Anestesiologia. Publicado por Elsevier Editora Ltda. All rights reserved.)

Objective: To evaluate the possible association between the scale of Alvarado (EA) and macroscopic appearance (MA) of the appendix in patients with acute appendicitis., Methods: after receiving the diagnosis of acute appendicitis, EA data were collected. During appendectomy, MA data were collected. Data from patients without appendicitis were excluded. The Spearman correlation test was used to compare EA with Appendix MA (p < 0.05). Other variables were represented by simple frequency. The confidence interval (CI) of 95% was calculated for the correlation test., Results: Data were collected from 67 consecutive patients. The mean age was 37.1 ± 12.5 years and 77.6% of patients were male. The Spearman correlation test used for EA and MA was + 0.77 (95% CI 0.65-0.85, p < 0.0001)., Conclusion: although correlation was not perfect, our data indicate that a high score on the scale of Alvarado in patients with appendicitis is correlated with advanced stages of the inflammatory process of acute appendicitis.

Streetscapes are basic urban elements which play a major role in the livability of a city. The visual complexity of streetscapes is known to influence how people behave in such built spaces. However, how and which characteristics of a visual scene influence our perception of complexity have yet to be fully understood. This study proposes a method to evaluate the complexity perceived in streetscapes based on the statistics of local contrast and spatial frequency. Here, 74 streetscape images from four cities, including daytime and nighttime scenes, were ranked for complexity by 40 participants. Image processing was then used to locally segment contrast and spatial frequency in the streetscapes. The statistics of these characteristics were extracted and later combined to form a single objective measure. The direct use of statistics revealed structural or morphological patterns in streetscapes related to the perception of complexity. Furthermore, in comparison to conventional measures of visual complexity, the proposed objective measure exhibits a higher correlation with the opinion of the participants. Also, the performance of this method is more robust regarding different time scenarios.

Background: Female breast cancer is the major cause of death by cancer in western countries. Efforts in Computer Vision have been made in order to improve the diagnostic accuracy by radiologists. Some methods of lesion diagnosis in mammogram images were developed based in the technique of principal component analysis which has been used in efficient coding of signals and 2D Gabor wavelets used for computer vision applications and modeling biological vision., Methods: In this work, we present a methodology that uses efficient coding along with linear discriminant analysis to distinguish between mass and non-mass from 5090 region of interest from mammograms., Results: The results show that the best rates of success reached with Gabor wavelets and principal component analysis were 85.28% and 87.28%, respectively. In comparison, the model of efficient coding presented here reached up to 90.07%., Conclusions: Altogether, the results presented demonstrate that independent component analysis performed successfully the efficient coding in order to discriminate mass from non-mass tissues. In addition, we have observed that LDA with ICA bases showed high predictive performance for some datasets and thus provide significant support for a more detailed clinical investigation.

The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

Consciousness has been a subject of crescent interest among the neuroscience community. However, building machine models of it is quite challenging, as it involves many characteristics and properties of the human brain which are poorly defined or are very abstract. Here I propose to use information theory (IT) to give a mathematical framework to understand consciousness. For this reason, I used the term "computational". This work is grounded on some recent results on the use of IT to understand how the cortex codes information, where redundancy reduction plays a fundamental role. Basically, I propose a system, here called "organism", whose strategy is to extract the maximal amount of information from the environment in order to survive. To highlight the proposed framework, I show a simple organism composed of a single neuron which adapts itself to the outside dynamics by taking into account its internal state, whose perception is understood here to be related to "feelings".

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.

The detection of the basic electric rhythm (BER), composed of a 3 cycles min(-1) oscillation, can be performed using SQUID magnetometers. However, the electric response activity (ERA), which is generated when the stomach is performing a mechanical activity, was detected mainly by invasive electrical measurements and only recently was one report published describing its detection by magnetic measurements. This study was performed with the aim of detecting the ERA noninvasively after a meal. MGG recordings were made with a 74-channel first-order gradiometer (Magnes II, biomagnetic technologies) housed in a shielded room. Seven nonsymptomatic volunteers were measured in the study. Initially a 10 min recording was performed with the subject in the fasted state. A 250 kcal meal was given to the subject without moving out of the magnetometers and two epochs of 10 min each were acquired. The signals were processed to remove cardiac interference by an algorithm based on a variation of independent component analysis (ICA), then autoregressive and wavelet analysis was performed. Preliminary results have shown that there is an increase in the signal power at higher frequencies around (0.6 Hz-1.3 Hz) usually associated with the basic electric rhythm. The center of the frequency band and its width varied from subject to subject, demonstrating the importance of pre-prandial acquisition as a control. Another interesting finding was an increase in power after about 5 min of meal ingestion. This period roughly agrees with the lag phase of gastric emptying, measured by scintigraphy and other techniques. We confirm that MGG can detect the electric response activity in normal volunteers. Further improvements in signal processing and standardization of signal acquisition are necessary to ascertain its possible use in clinical situations.

Objectives: This paper proposes an efficient method for the discrimination and classification of mammograms with benign, malignant and normal tissues., Methods: The proposed method consists of selection of tissues, feature extraction using independent component analysis, feature selection by the forward-selection technique and classification of the tissue by the multilayer perceptron., Results: The method is tested for a mammogram set of the MIAS database, resulting in a 97.83% success rate, with 98.0% specificity and 97.5% sensitivity., Conclusion: The proposed method showed a good classification rate. The method will be useful for early cancer diagnosis.

Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface(BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble averaging is not suitable for BCI. In order to process raw EEG data in real time, we performed saccade-related EEG experiments and processed data by using the non-conventional Fast ICA with Reference signal(FICAR). Visually guided saccade tasks and auditorily guided saccade tasks were performed and the EEG signal generated in the saccade was recorded. Saccade-related EEG signals and saccade-related ICs in visually and Auditorily guided saccade task are compared in the point of the latency between starting time of a saccade and time when a saccade-related EEG signal or an IC has maximum value and in the point of the peak scale where a saccade-related EEG signal or an IC has maximum value. As results, peak time when saccade-related ICs have maximum amplitude is earlier than peak time when saccade-related EEG signals have maximum amplitude. This is very important advantage for developing our BCI. However, S/N ratio in being processed by FICAR is not improved comparing S/N ratio in being processed by ensemble averaging. In next step, we tried to estimate direction of saccade from raw EEG signals by FICAR.

Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface (BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble averaging is not suitable for BCI. In order to process raw EEG data in real time, we performed saccade-related EEG experiments and processed data by using the non-conventional fast ICA with reference signal (FICAR). The FICAR algorithm can extract desired independent components (IC) which have strong correlation against a reference signal. Visually guided saccade tasks and auditory guided saccade tasks were performed and the EEG signal generated in the saccade was recorded. The EEG processing was performed in three stages: PCA preprocessing and noise reduction, extraction of the desired IC using Wiener filter with reference signal, and post-processing using higher order statistics fast ICA based on maximization of kurtosis. Form the experimental results and analysis we found that using FICAR it is possible to extract form raw EEG data the saccade-related ICs and to predict saccade in advance by about 10 [ms] before real movements of eyes occurs. For single trail EEG data we have successfully extracted the desire ICs with recognition rate about 70%. In next steps, saccade-related EEG signals and saccade-related ICs in visually and auditory guided saccade task are compared in the point of the latency between starting time of a saccade and time when a saccade-related EEG signal or an IC has maximum value and in the point of the peak scale where a saccade-related EEG signal or an IC has maximum value. As results, peak time when saccade-related ICs have maximum amplitude is earlier than peak time when saccade-related EEG signals have maximum amplitude. This is very important advantage for developing our BCI. However, S/N ratio in being processed by FICAR is not improved comparing S/N ratio in being processed by ensemble averaging.

Background: Due to its easy applicability, pulse wave has been proposed as a surrogate of electrocardiogram (ECG) for the analysis of heart rate variability (HRV). However, its smoother waveform precludes accurate measurement of pulse-to-pulse interval by fiducial-point algorithms. Here we report a pulse frequency demodulation (PFDM) technique as a method for extracting instantaneous pulse rate function directly from pulse wave signal and its usefulness for assessing pulse rate variability (PRV)., Methods: Simulated pulse wave signals with known pulse interval functions and actual pulse wave signals obtained from 30 subjects with a trans-dermal pulse wave device were analyzed by PFDM. The results were compared with heart rate and HRV assessed from simultaneously recorded ECG., Results: Analysis of simulated data revealed that the PFDM faithfully demodulates source interval function with preserving the frequency characteristics of the function, even when the intervals fluctuate rapidly over a wide range and when the signals include fluctuations in pulse height and baseline. Analysis of actual data revealed that individual means of low and high frequency components of PRV showed good agreement with those of HRV (intraclass correlation coefficient, 0.997 and 0.981, respectively)., Conclusion: The PFDM of pulse wave signal provides a reliable assessment of PRV. Given the popularity of pulse wave equipments, PFDM may open new ways to the studies of long-term assessment of cardiovascular variability and dynamics.

Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.

The purpose of this study was to assess the validity of an adaptive filter, the scaled Fourier linear combiner (SFLC), in the impedance cardiography (ICG). Eight healthy males underwent constant-load bicycle exercise at different intensities from unloaded to near maximal intensity. The stroke volume (SV) and cardiac output (Q) measured by ICG at each condition were compared with those by the CO2 rebreathing method. We found that the noises were greatly reduced in the impedance waveform and that the inflection points, so-called the B- and X-points, were clearly detected even during strenuous exercise using the SFLC. Although a high correlation was observed between Qs measured by the two methods, the mean values of Qs in each method differed significantly and the regression line also differed significantly from the identity line. Likewise, a significant correlation was observed between SVs obtained by the two methods, but a significant difference in the group mean values and a trend of the regression line were observed. These findings suggest that the use of SFLC in ICG improves the performance in eliminating the noises and in detecting the inflection points in the waveform, thereby contributing to the accurate and beat-to-beat measurements of SV and Q especially during exercise.

Redundancy reduction as a form of neural coding has been since the early sixties a topic of large research interest. A number of strategies has been proposed, but the one which is attracting most attention recently assumes that this coding is carried out so that the output signals are mutually independent. In this work we go one step further and suggest an strategy to deal also with non-orthogonal signals (i.e., "dependent" signals). Moreover, instead of working with the usual squared error, we design a neuron where the non-linearity is operating on the error. It is computationally more economic and, importantly, the permutation/scaling problem is avoided. The framework is given with a biological background, as we avocate throughout the manuscript that the algorithm fits well the single neuron and redundancy reduction doctrine. Moreover, we show that wavelet-like receptive fields emerges from natural images processed by this algorithm.

In this paper, we develop a system for enhancement of the speech signal with highest energy from a linear convolutive mixture of n statistically independent sound sources recorded by m microphones, where m

In this work we develop a very simple batch learning algorithm for semiblind extraction of a desired source signal with temporal structure from linear mixtures. Although we use the concept of sequential blind extraction of sources and independent component analysis, we do not carry out the extraction in a completely blind manner; neither do we assume that sources are statistically independent. In fact, we show that the a priori information about the autocorrelation function of primary sources can be used to extract the desired signals (sources of interest) from their linear mixtures. Extensive computer simulations and real data application experiments confirm the validity and high performance of the proposed algorithm.

Our study focuses on a new method of estimating the heart rate variability (HRV) which does not require the use of electrocardiogram (ECG) R-wave detection. Contrary to the R-wave detection method which requires a sampling frequency higher than 100 Hz, the one proposed here can be used to calculate the HRV from an ECG signal sampled at a frequency of approximately 5 Hz with a relative mean error of 0.03. This new method is based on extracting the instantaneous fundamental frequency from the ECG. The method could be efficiently used to extract the HRV from an ECG measured for healthy subjects performing an exercise in which the HRV increases linearly with time, and for subjects with respiratory and cardiac problems. The overall error decreased as we low-pass filtered the HRV with lower cut-off frequencies. Moreover, it was shown that the method could be efficiently used to calculate the HRV from blood pressure measurements and to be robust to noise.

Independent component analysis (ICA) is a powerful tool for separating signals from their mixtures. In this field, many algorithms were proposed, but they poorly use a priori information in order to find the desired signal. Here, we propose a fixed point algorithm which uses a priori information to find the signal of interest out of a number of sensors. We particularly applied the algorithm to cancel cardiac artifacts from a magnetoencephalogram.

The mean-squared error (MSE) behavior for Fourier linear combiner (FLC)-based filters is analyzed, using the independence assumption. The advantage of this analysis is its simplicity compared with previous results. The MSE transient behavior for this kind of filters is also presented for the first time. Moreover, a time-varying sequence for the least mean square (LMS) algorithm step-size is proposed to provide fast convergence with small misadjustment error. It is shown that for this sequence, the MSE behaves better as the input signal-to-noise ratio (SNR) decreases, but increases with the number of harmonics. Lastly, we make a brief analysis on the nonstationary behavior of these filters, and again we find simple expressions for the MSE behavior.

Impedance cardiography (ICG) may be altered by noises as respiration and movement artifacts, mainly during exercise. In this work, a scaled Fourier linear combiner (SFLC) event-related to the R-R interval of ECG is proposed. It estimates the deterministic component of the impedance cardiographic signal and removes the noises uncorrelated to this interval. The impedance cardiographic signal is modeled as Fourier series with the coefficients estimated by the least mean square (LMS) algorithm. Simulations have been carried out to evaluate the filter performance for different noise conditions. Moreover, the method capability to remove uncorrelated noises was also examined in physiological data obtained in rest and exercise, by synchronizing respiration and pedaling with a metronome. Analyzing the ICG power spectrum, it was concluded that the proposed filter could remove the noises that are not synchronized with heart rate.