Histopatological effects of bleaching and disease on the coral Siderastrea stellata from coastal reefs of Brazil

ABSTRACT Histological analysis of bleached samples of Siderastrea stellata Verrill, 1868, with white plague disease and with alteration in the color pattern, revealed drastic tissue and cellular disturbances, such as lysis of the external epithelium, hyperplasia of gastrodermis, apoptosis of epithelial cells and zooxanthellae, and degradation of mesenteric filaments and reproductive cells. Fungal hyphae, diatoms, and cyanobacteria were present in diseased samples and colonies with alteration in the color pattern. Furthermore, bleached and diseased samples showed significant reductions in the number of zooxanthellae per 100 µm2 of gastrodermis and significant reductions in the diameters and volumes of stage V oocytes. We found that bleaching events, diseases, and colonies with alteration in the color pattern promoted degradation of reproductive cells, resulting in the consequent interruption of the reproduction of the coral S. stellata, which is one of the most common Brazilian coral species. The implications of these indirect effects of bleaching, disease, and changes in coral color patterns in the population dynamics of Brazilian reefs are discussed.

Non-Invasive Identification of Atrial Fibrillation Driver Location Using the 12-lead ECG: Pulmonary Vein Rotors vs. other Locations

Atrial fibrillation (AF) is an irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. In the present work, we sought to characterize and discriminate whether simulated single stable rotors are located in the pulmonary veins (PVs) or not, only by using non-invasive signals (i.e., the 12-lead ECG). Several features have been extracted from the signals, such as Hjort descriptors, recurrence quantification analysis (RQA), and principal component analysis. All the extracted features have shown significant discriminatory power, with particular emphasis to the RQA parameters. A decision tree classifier achieved 98.48% accuracy, 83.33% sensitivity, and 100% specificity on simulated data.Clinical Relevance-This study might guide ablation procedures, suggesting doctors to proceed directly in some patients with a pulmonary veins isolation, and avoiding the prior use of an invasive atrial mapping system.

236Automatic classification of 20 different types of atrial tachycardia using 12-lead ECG signals

Funding Acknowledgements

Supported by the European Union"s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.766082 (MY-ATRIA)

Background

 Atrial Flutter (AFl) as a common reentrant atrial tachycardia is driven by self-sustainable mechanisms that cause excitation to propagate along pathways different from sinus rhythm. Intracardiac electrophysiological mapping and catheter ablation is often performed without prior knowledge of the mechanism perpetuating AFl in a given patient, likely prolonging the procedure time of these invasive interventions. We investigated the feasibility of automatically identifying 20 different AFl types based on the non-invasive 12-lead electrocardiogram (ECG) using machine learning. 

Methods

 Electrophysiological fast marching computer simulations of 20 different atrial tachycardia scenarios (micro-/macro-reentry, scar-related/anatomical/functional, figure-of-eight, focal, different locations) were performed and propagated to the standard 12-lead ECG based on the Courtemanche atrial action potential model. The virtual study population comprised combinations of 8 different anatomical bi-atrial models with 2 orientational variants each and 8 different torso models yielding a total of 2512 ECGs. From each ECG, we extracted 114 features from different domains (e.g., time, frequency, entropy, wavelet, non-linear recurrence analysis). The dataset was randomly split into 1256 training samples, 628 validation samples and 628 test samples while maintaining a balanced AFl type distribution. A radial basis neural network (RBNN) was trained as a classifier after selection of the most informative features. 

Results

The RBNN yielded a test set accuracy of 90% regarding the identification of the AFl mechanism using 10 features (from different domains). The most discriminative single feature was the cycle length that alone led to a test set accuracy of 74%, while the remaining feature set without cycle length (9 features) reduced the test set accuracy to 33%. The machine learning approach generalized well regarding unseen torso geometries (90% accuracy if training was performed on only 7 torso models) but rather poor regarding atrial anatomies (23% if the atrial anatomical model was not seen during training) indicating that more than the currently used 8 atrial models should be included during training to cover the relevant anatomical variability. 

Conclusions

Our results show that a machine learning classifier can potentially identify a high number of different AFl types using the 12-lead ECG. This non-invasive method can aid in planning and tailoring AFl treatment for patients. Application to clinical data is necessary as a next step to pave the way for clinical translation.

Abstract Figure.

Assessment of spatial heterogeneity of ventricular repolarization after multi-channel blocker drugs in healthy subjects

BACKGROUND AND OBJECTIVES

In contrast to potassium channel blockers, drugs affecting multiple channels seem to reduce torsadogenic risks. However, their effect on spatial heterogeneity of ventricular repolarization (SHVR) is still matter of investigation. Aim of this work is to assess the effect of four drugs blocking the human ether-à-go-go-related gene (hERG) potassium channel, alone or in combination with other ionic channel blocks, on SHVR, as estimated by the V-index on short triplicate 10 s ECG.

METHODS

The V-index is an estimate of the standard deviation of the repolarization times of the myocytes across the entire myocardium, obtained from multi-lead surface electrocardiograms. Twenty-two healthy subjects received a pure hERG potassium channel blocker (dofetilide) and 3 other drugs with additional varying degrees of sodium and calcium (L-type) channel block (quinidine, ranolazine, and verapamil), as well as placebo. A one-way repeated-measures Friedman test was performed to compare the V-index over time.

RESULTS

Computer simulations and Bland-Altman analysis supported the reliability of the estimates of V-index on triplicate 10 s ECG. Ranolazine, verapamil and placebo did not affect the V-index. On the contrary, after quinidine and dofetilide administration, an increase of V-index from predose to its peak value was observed (ΔΔV-index values were 19 ms and 27 ms, respectively, p < 0.05).

CONCLUSIONS

High torsadogenic drugs (dofetilide and quinidine) affected significantly the SHVR, as quantified by the V-index. The metric has therefore a potential in assessing drug arrhythmogenicity.

Workload Monitoring in Top-level Soccer Players During Congested Fixture Periods

This study assessed the internal and external workload of starters and non-starters in a professional top-level soccer team during a congested fixture period. Twenty Serie A soccer players were monitored in this study during two mesocycles of 21 days each. Starters and non-starters were divided based on the match time played in each mesocycle. The following metrics were recorded: exposure time, total distance, relative total distance, high-speed running distance over 20 km·h^-1, very high-speed running distance over 25 km·h^-1, individual very high-speed distance over 80% of maximum peak speed, and rating of perceived exertion. Differences between starters and non-starters were found for: exposure time (effect size=large to very large), rating of perceived exertion (large to very large), total distance (large to very large), and individual very high-speed distance over 80% of maximum peak speed (moderate to large). Furthermore, differences for relative total distance, high-speed running distance over 20 km·h^-1 and very high-speed running distance over 25 km·h^-1 were small to moderate, but not significant. This study reports that during congested fixture periods, starters had higher exposure time, rating of perceived exertion, total distance, and individual very high-speed distance over 80% of maximum peak speed than non-starters.

Strategies and Solutions for Team Sports Athletes in Isolation due to COVID-19

In December of 2019, there was an outbreak of a severe acute respiratory syndrome caused by the Coronavirus 2 (SARS-CoV-2 or COVID-19) in China. The virus rapidly spread into the whole World causing an unprecedented pandemic and forcing governments to impose a global quarantine, entering an extreme unknown situation. The organizational consequences of quarantine/isolation are: absence of organized training and competition, lack of communication among athletes and coaches, inability to move freely, lack of adequate sunlight exposure, inappropriate training conditions. Based on the current scientific, we strongly recommend encouraging the athlete to reset their mindset to understand quarantine as an opportunity for development, organizing appropriate guidance, educating and encourage athletes to apply appropriate preventive behavior and hygiene measures to promote immunity and ensuring good living isolation conditions. The athlete's living space should be equipped with cardio and resistance training equipment (portable bicycle or rowing ergometer). Some forms of body mass resistance circuit-based training could promote aerobic adaptation. Sports skills training should be organized based on the athlete's needs. Personalized conditioning training should be carried out with emphasis on neuromuscular performance. Athletes should also be educated about nutrition (Vitamin D and proteins) and hydration. Strategies should be developed to control body composition. Mental fatigue should be anticipated and mental controlled. Adequate methods of recovery should be provided. Daily monitoring should be established. This is an ideal situation in which to rethink personal life, understanding the situation, that can be promoted in these difficult times that affect practically the whole world.

Refined Ventricular Activity Cancellation in Electrograms During Atrial Fibrillation by Combining Average Beat Subtraction and Interpolation

Many techniques have been developed to cancel the ventricular interference in atrial electrograms (AEG) during atrial fibrillation. In particular, average beat subtraction (ABS) and interpolation are among those mostly adopted. However, ABS usually leaves high power residues and discontinuity at the borders, whereas interpolation totally substitutes the residual activity with a forecasting that might fail at the center of the cancellation segment. In this study, we proposed a new algorithm to refine the ventricular estimate provided by ABS, in such a way that the residual activity should likely be distributed as the local atrial activity. Briefly, the local atrial activity is first modeled with an autoregressive (AR) process, then the estimate is refined by maximizing the log likelihood of the atrial residual activity according to the fitted AR model. We tested the new algorithm on both synthetic and real AEGs, and compared the performance with other four algorithms (two variants of ABS, interpolation and zero substitution). On synthetic data, our algorithm outperformed all the others in terms of average root mean square error (0.043 vs 0.046 for interpolation; p <; 0.05). On real data, our methodology outperformed two variants of ABS (p <; 0.05) and performed similarly to interpolation when considering the high power residues left (both <; 5%), and the log likelihood with the fitted AR model.

Quantification of Spatial Heterogeneity of Ventricular Repolarization During Early-Stage Cardiac Ischemia Induced by Coronary Angioplasty

Coronary angioplasty (CA) is a surgical procedure meant to break the plaque and restore the blood flow in obstructed coronary arteries. It is based on inserting an inflatable balloon with a catheter in the clogged artery. When the balloon inflation is prolonged, it also provides an excellent model to investigate the electrophysiological changes due to early ischemia. In this work, we tested whether early cardiac ischemia induced by prolonged balloon inflations might lead to changes in spatial heterogeneity of ventricular repolarization (SHVR), as measured by the V-index on the 12-lead ECG. The metric was recently shown to significantly improve the ECG sensitivity for the diagnosis of non-ST elevation myocardial infarction, in patients presenting to the emergency department. The analysis was retrospectively performed on the data of 104 patients who underwent prolonged CA (STAFF III dataset). The V-index was estimated before, during and post-occlusion (limiting the analysis to the first inflation). Successively, it was quantified on short 90 s overlapping windows, during occlusion, to assess the time evolution of SHVR. V-index values estimated during occlusion were significantly larger (median: 6.2 ms, p <; 0.05) than baseline room values. Also, pre- and post-occlusion values did not differ (p > 0.05), suggesting a complete recovery after CA. SHVR progressively increased during the occlusion with respect to baseline (median reaching 55.6 ms vs 34.2 ms). In conclusion, the V-index detected changes in SHVR due to early-stage cardiac ischemia.

Mixotrophic cultivation of Spirulina platensis in dairy wastewater: Effects on the production of biomass, biochemical composition and antioxidant capacity

Mixotrophic cultivation of microalgae provides a very promising alternative for producing carbohydrate-rich biomass to convert into bioethanol and value-added biocompounds, such as vitamins, pigments, proteins, lipids and antioxidant compounds. Spirulina platensis may present high yields of biomass and carbohydrates when it is grown under mixotrophic conditions using cheese whey. However, there are no previous studies evaluating the influence of this culture system on the profile of fatty acids or antioxidant compounds of this species, which are extremely important for food and pharmaceutical applications and would add value to the cultivation process. S. platensis presented higher specific growth rates, biomass productivity and carbohydrate content under mixotrophic conditions; however, the antioxidant capacity and the protein and lipid content were lower than that of the autotrophic culture. The maximum biomass yield was 2.98 ±0.07 g/L in growth medium with 5.0% whey. The phenolic compound concentration was the same for the biomass obtained under autotrophic and mixotrophic conditions with 2.5% and 5.0% whey. The phenolic compound concentrations showed no significant differences except for that in the growth medium with 10.0% whey, which presented an average value of 22.37±0.14 mg gallic acid/g. Mixotrophic cultivation of S. platensis using whey can be considered a viable alternative to reduce the costs of producing S. platensis biomass and carbohydrates, shorten cultivation time and produce carbohydrates, as it does not require adding expensive chemical nutrients to the growth medium and also takes advantage of cheese whey, an adverse dairy industry byproduct.

Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method

BACKGROUND

Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls.

METHODS

In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty.

RESULTS

Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD.

LIMITATIONS

The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected.

CONCLUSIONS

The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease.

Theoretical Value of Deceleration Capacity Points to Deceleration Reserve of Fetal Heart Rate

OBJECTIVE

The interpretation of Average Acceleration and Deceleration Capacities (AC/DC), computed through Phase-Rectified Signal Averaging (PRSA), in intrapartum fetal heart rate (FHR) monitoring is still matter of investigation. We aimed to elucidate some behaviors of AC/DC.

METHODS

We derived the theoretical value of PRSA for stationary stochastic Gaussian processes and proved that for these time series AC and DC are necessarily identical in absolute value. The difference between DC and AC, termed Deceleration Reserve (DR), was introduced to detect signal's asymmetric trends. DR was tested on FHR signals from: near-term pregnant sheep model of labor consisting of chronically hypoxic and normoxic fetuses with both groups developing acidemia due to umbilical cord occlusions (UCO); and the CTU-UHB dataset containing fetal CTG recordings collected during labor of newborns that resulted acidotic and non-acidotic, respectively. DR was compared with AC and DC in terms of discriminatory power (AUC), between the groups, after correcting for signal power or deceleration area, respectively.

RESULTS

DR displayed higher discriminatory power on the animal model during severe acidemia, with respect to AC/DC ( ) but also distinguished correctly all chronically hypoxic from normoxic fetuses at baseline prior to UCO. DR also outperformed AC/DC on the CTU-UHB dataset in distinguishing acidemic fetuses at birth (AUC: 0.65).

CONCLUSION

Theoretical results motivated the introduction of DR, that proved to be superior than AC/DC for risk stratification during labor.

SIGNIFICANCE

DR, measured during labor, might permit to distinguish acidemic fetuses due to their different autonomic regulation, paving the way for new monitoring strategies.

Trends and Predictors of Repeat Mental Health Visits to a Pediatric Emergency Department in Hamilton, Ontario

OBJECTIVE

From 2007-2017, pediatric emergency department (ED) visits for mental health concerns increased by 66% in Canada, with repeat visits accounting for a significant proportion of all visits. Our objective was to examine patient and visit characteristics associated with repeat visits to a tertiary care pediatric ED for mental health concerns.

METHOD

Data were obtained from the administrative records of McMaster Children's Hospital ED for mental health-related visits from February 2013-December 2017. Data on 9,018 ED visits made by 4,976 unique patients were included in this study. Logistic regression analysis was used to examine characteristics associated with repeat visit within six months of index presentation.

RESULTS

22% (n=1,088) of individuals returned to the ED for a mental health concern within six months following their index visit. A repeat visit within six months was associated with female sex (OR=1.19, p=0.019), age of 14-17 years (OR=1.42, p=0.016), receiving a risk assessment by the emergency psychiatry team (OR=1.63, p<0.001) and having an inpatient psychiatric admission (OR=1.67, p<0.001) at the index visit. Receiving anxiety-related discharge diagnoses at an index visit reduced the odds of a repeat visit within 6 months (OR=0.76, p=0.035), while receiving depression-related discharge diagnoses increased the odds of a repeat visit, but only for females (OR=1.3, p=0.011 vs. OR=0.93, p=0.589 for males).

CONCLUSIONS

We found that approximately one in five patients presenting to the ED for a mental health concern have a repeat visit within six months, consistent with previous studies. This study provides support for previously identified risk factors for repeat visits and offers information on interactions between patient sex and diagnosis.

Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis

Gait and balance disorders are among the main predisposing factors of falls in elderly. Clinical scales are widely employed to assess the risk of falling, but they require trained personnel. We investigate the use of objective measures obtained from a wearable accelerometer to evaluate the fall risk, determined by the Tinetti clinical scale. Seventy-nine patients and eleven volunteers were enrolled in two rehabilitation centers and underwent a full Tinetti test, while wearing a triaxial accelerometer at the chest. Tinetti scores were assessed by expert physicians and those subjects with a score ≤18 were considered at high risk. First, we analyzed 21 accelerometer features by means of statistical tests and correlation analysis. Second, one regression and one classification problem were designed and solved using a linear model (LM) and an artificial neural network (ANN) to predict the Tinetti outcome. Pearson's correlation between the Tinetti score and a subset of 9 features (mainly related with standing and walking) was 0.71. The misclassification error of high risk patient was 0.21 and 0.11, for LM and ANN, respectively. The work might foster the development of a new generation of applications meant to monitor the time evolution of the fall risk using low cost devices at home.

Effectiveness of nutrient remotion and cell physiology of Amphora sp. cultured in shrimp farm effluents

Shrimp farm effluents are one of the principal causes of eutrophication in coastal environments. Integrated processes of bioremediation involving the culturing of purifying organisms have been suggested, but very few studies have focused on microalgae. For that purpose evaluated the growth potential of Amphora sp. in the residual waters of shrimp farm activity fulfilled on the Paraíba State, Brazil. The experiments were performed using Conway medium as the control and wastewaters from shrimp farm at 100% concentrations. Amphora sp. demonstrated good growth in the shrimp farm effluents under test conditions, although less than that observed in the control medium. This diatom was found to removed 73.357 and 72.572% of PO₄^- and NO₃^-, respectively, demonstrating a high mitigation potential for this type of effluent. Comparisons of the physiological responses measured by flow cytometry demonstrated higher cell densities of Amphora sp. in the control medium, but a higher lipid content was observed in Amphora cultured in shrimp farm effluents.

Parametric estimation of sample entropy for physical activity recognition

Insufficient amount of physical activity, and hence storage of calories may lead depression, obesity, cardiovascular diseases, and diabetes. The amount of consumed calorie depends on the type of activity. The recognition of physical activity is very important to estimate the amount of calories spent by a subject every day. There are some research works already published in the literature for activity recognition through accelerometers (body worn sensors). The accuracy of any recognition system depends on the robustness of selected features and classifiers. The typical features reported for most physical activities recognitions are autoregressive coefficients (ARcoeffs), signal magnitude area (SMA), tilt angle (TA), and standard deviation (STD). In this study, we have studied the feasibility of using single value of sample entropy estimated parametrically (SETH) of an AR model instead of ARcoeffs. After feasibility study, we also compared the recognition accuracies between two popular classifiers ı.e. artificial neural network (ANN) and support vector machines (SVM). The recognition accuracies using linear structure (where all types of activities are classified using a single classifier) and hierarchical structure (where activities are first divided into static and dynamic events, and then activities of each event are classified in the second stage). The study showed that the use of SETH provides similar recognition accuracy (69.82%) as provided by ARcoeffs (67.67%) using ANN. The linear structure of SVM performs better (average accuracy of SVM: 98.22%) than linear ANN (average accuracy with ANN: 94.78%). The use of hierarchical structure of ANN increases the average recognition accuracy of static activities to about 100%. However, no significant changes are observed using hierarchical SVM than the linear one.

Bubble Entropy: An Entropy Almost Free of Parameters

Objective: A critical point in any definition of entropy is the selection of the parameters employed to obtain an estimate in practice. We propose a new definition of entropy aiming to reduce the significance of this selection. Methods: We call the new definition Bubble Entropy. Bubble Entropy is based on permutation entropy, where the vectors in the embedding space are ranked. We use the bubble sort algorithm for the ordering procedure and count instead the number of swaps performed for each vector. Doing so, we create a more coarse-grained distribution and then compute the entropy of this distribution. Results: Experimental results with both real and synthetic HRV signals showed that bubble entropy presents remarkable stability and exhibits increased descriptive and discriminating power compared to all other definitions, including the most popular ones. Conclusion: The definition proposed is almost free of parameters. The most common ones are the scale factor r and the embedding dimension m . In our definition, the scale factor is totally eliminated and the importance of m is significantly reduced. The proposed method presents increased stability and discriminating power. Significance: After the extensive use of some entropy measures in physiological signals, typical values for their parameters have been suggested, or at least, widely used. However, the parameters are still there, application and dataset dependent, influencing the computed value and affecting the descriptive power. Reducing their significance or eliminating them alleviates the problem, decoupling the method from the data and the application, and eliminating subjective factors.Objective: A critical point in any definition of entropy is the selection of the parameters employed to obtain an estimate in practice. We propose a new definition of entropy aiming to reduce the significance of this selection. Methods: We call the new definition Bubble Entropy. Bubble Entropy is based on permutation entropy, where the vectors in the embedding space are ranked. We use the bubble sort algorithm for the ordering procedure and count instead the number of swaps performed for each vector. Doing so, we create a more coarse-grained distribution and then compute the entropy of this distribution. Results: Experimental results with both real and synthetic HRV signals showed that bubble entropy presents remarkable stability and exhibits increased descriptive and discriminating power compared to all other definitions, including the most popular ones. Conclusion: The definition proposed is almost free of parameters. The most common ones are the scale factor r and the embedding dimension m . In our definition, the scale factor is totally eliminated and the importance of m is significantly reduced. The proposed method presents increased stability and discriminating power. Significance: After the extensive use of some entropy measures in physiological signals, typical values for their parameters have been suggested, or at least, widely used. However, the parameters are still there, application and dataset dependent, influencing the computed value and affecting the descriptive power. Reducing their significance or eliminating them alleviates the problem, decoupling the method from the data and the application, and eliminating subjective factors.

Low Computational Cost for Sample Entropy

Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regularity/complexity of a time series. On the other hand, it is a computationally expensive method which may require a large amount of time when used in long series or with a large number of signals. The computationally intensive part is the similarity check between points in m dimensional space. In this paper, we propose new algorithms or extend already proposed ones, aiming to compute Sample Entropy quickly. All algorithms return exactly the same value for Sample Entropy, and no approximation techniques are used. We compare and evaluate them using cardiac inter-beat (RR) time series. We investigate three algorithms. The first one is an extension of the kd-trees algorithm, customized for Sample Entropy. The second one is an extension of an algorithm initially proposed for Approximate Entropy, again customized for Sample Entropy, but also improved to present even faster results. The last one is a completely new algorithm, presenting the fastest execution times for specific values of m, r, time series length, and signal characteristics. These algorithms are compared with the straightforward implementation, directly resulting from the definition of Sample Entropy, in order to give a clear image of the speedups achieved. All algorithms assume the classical approach to the metric, in which the maximum norm is used. The key idea of the two last suggested algorithms is to avoid unnecessary comparisons by detecting them early. We use the term unnecessary to refer to those comparisons for which we know a priori that they will fail at the similarity check. The number of avoided comparisons is proved to be very large, resulting in an analogous large reduction of execution time, making them the fastest algorithms available today for the computation of Sample Entropy.

Linear-Sigmoidal modelling of accelerometer features and Tinetti score for automatic fall risk assessment

Falling in elderly is a worldwide major problem and it can lead to severe injuries or death. Despite the effort made to ensure home environments safe and foster healthy lifestyles, it is still necessary to provide methodologies that can be used at home for detect risk factors associated with falls. In this study, we proposed a new simple non-linear model, i.e., Linear-Sigmoidal model (LS), easy to fit and simple to interpret, used to model accelerometer features and outcome of the clinical scale Tinetti (clinical scale for fall risk prediction). Also, subjects with a score ≤ 18 were considered as high risk of falling. One-hundred-twelve subjects underwent to a Tinetti test while wearing a 3D axis accelerometer at the chest, and the Tinetti score used as gold standard. Ninety subjects were used as training set and twenty-two ones were employed to test the model. The same sets were used to assess the performance of the standard linear regression (LR). Seven accelerometer features and the body mass index were used in the model regression. LS resulted better than LR in terms of model agreement (R²: 0.76 vs 0.72) and classification accuracy (0.91 vs 0.86) on the test set.

Microcystins production and antibacterial activity of cyanobacterial strains of Synechocystis, Synechococcus and Romeria isolated from water and coral reef organisms of Brazilian coast

Cyanobacteria are widely distributed in terrestrial, freshwater and marine environments, and over the past decades have been recognized as a powerful source of bioactive compounds. In this study, some cyanobacterial strains were isolated from samples of seawater, brackish water and tissue of reef benthic invertebrates (zoanthid Protopalythoa variabilis, the sponges Cynachrella sp. and Haliclona sp., the coral Siderastrea stellata, and ascidians), collected at the states of Paraíba and Rio Grande do Norte (Northeast of Brazil), during the period between July 2010 and February 2014. After standard isolation methods, the cultivation of the strains was carried out in acclimatized culture chamber (25 °C) under constant aeration, for 15 days at 12-hour photoperiod, using Conway and BG11 media made with filtered seawater. After ethanolic and methanolic extracts, the strains were analysed for the microcystin production by the ELISA technique and for the antibacterial activity against Staphylococcus aureus and Pseudomonas aeruginosa by the agar well diffusion method. The detection of the mcyB gene, one of the genes related to the microcystin synthesis, was done by the Polymerase Chain Reaction (PCR) technique. The majority of the eighteen cyanobacterial strains belonged to Synechococcaceae Family. The genera of Synechocystis, Synechococcus and Romeria were represented by ten, six and two strains, respectively. The production of microcystins was observed in five strains belonging to the genus Synechocystis. The presence of mcyB gene was detected in 12 strains of cyanobacteria: Synechocystis (three strains), Synechococcus (six strains) and Romeria (two strains). Only one strain (Synechocystis aquatilis) showed both the microcystin production and the mcyB gene presence. The antibacterial activity was observed for one strain of Romeria gracilis, one strain of Synechocystis aquatilis and two strains of Synechococcus sp. The ethanolic extracts of R. gracilis strain and two Synechococcus spp. strains inhibited the growth of P. aeruginosa. Among methanolic extracts of cyanobacteria, only one strain of S. aquatilis showed activity against S. aureus, and one R. gracilis strain against P. aeruginosa. Some cyanobacterial strains studied were positive for the microcystin production and antibacterial activity against pathogenic bacteria S. aureus and P. aeuruginosa, and may be further explored for additional biotechnological applications.