In the present work, we evaluated the insecticidal activity of
(
) strains on
as an alternative for the organophosphate insecticide use in oil palm plantations in the Americas. The toxic effects of ...four
-strains (HD-1 var.
, SA-12 var.
, ABTS-1857 var.
, and GC-91 var.
) were evaluated against
caterpillars for toxicity, survival, anti-feeding, and mortality in field-controlled conditions. The
-strains, ABTS-1857 var.
(LC
= 0.84 mg mL
), GC-91 var.
(LC
= 1.13 mg mL
), and HD-1 var.
(LC
= 1.25 mg mL
), were the most toxic to
. The caterpillar survival was 99% without exposure to
-strains, and decreased to 52-23% in insects treated with the LC
and 10-1% in insects exposed to LC
after 48 h. Furthermore,
-strains decreased significantly the consumption of oil palm leaves of
3 h after exposure. Mortality of
caterpillars caused by
-strains had similar lethal effects in the laboratory and in field conditions. Our data suggest that
-strains have insecticidal activity against
and, therefore, have potential applications in oil palm pest management schemes.
Aim
To evaluate the impact of two non‐surgical periodontal treatment modalities on metabolic and periodontal clinical parameters in subjects with type 2 diabetes mellitus (T2DM) and poor glycaemic ...control and chronic periodontitis.
Material and methods
A randomized controlled clinical trial was conducted. Ninety‐three T2DM subjects with glycosylated haemoglobin (HbA1c) > 7% were randomly assigned to one of two groups receiving scaling with root planing in multiple sessions quadrant‐by‐quadrant (Q by Q) or within 24 hr (one stage). Periodontal parameters, HbA1c, glycaemia blood levels (FPG) and C‐reactive protein (CRP) values were assessed at baseline and at 3 and 6 months post‐therapy.
Results
At 6 months, HbA1c had decreased by 0.48% in the Q by Q group and by 0.18% in the one‐stage group (p = 0.455). After therapy, subjects with an initial HbA1c < 9% showed an increase of 0.31% (p = 0.145), compared with a decrease of 0.88% (p = 0.006) in those with an initial HbA1c ≥ 9%. Periodontal parameters improved significantly (p < 0.0001) post‐therapy, with similar results for both treatment modalities.
Conclusion
Periodontal therapy had the greatest impact on HbA1c reduction on patients with an HbA1c > 9% regardless of treatment modality. Both modalities resulted in significant improvements in periodontal parameters.
Tissue engineering using mesenchymal stem cells (MSCs) is a recent therapeutic modality that has several advantages. MSCs have high proliferation potential and may be manipulated to permit ...differentiation before being transplanted, suggesting they may be an ideal candidate for regenerative procedures. Precise identification of cells capable of regenerating the periodontium is valuable because no predictable regeneration procedure has yet been described. The purpose of this study is to determine the presence of MSCs in human gingival connective tissue and their morphologic and functional characteristics.
Gingival connective tissue samples were obtained from five healthy students. The samples were deepithelialized, leaving only connective tissue. The explants were minced and cultured on tissue culture dishes for 3 to 4 weeks, after which cells were characterized by flow cytometry. Differentiation into osteogenic, chondrogenic, and adipogenic lineages was induced and evaluated by culture staining. An immunoregulation assay was also performed.
The results show that gingival tissue cells fulfill the minimal criteria proposed by the International Society for Cellular Therapy to be defined as MSCs. Cell characterization was consistently positive for CD90, CD105, CD73, CD44, and CD13 markers and negative for hematopoietic markers CD34, CD38, CD45, and CD54. We observed differentiation in positive staining of adipogenic, chondrogenic, and osteogenic lineages. Furthermore, gingival cells showed immunomodulative capacity.
Gingival connective tissue could be a reservoir of MSCs that could be used in regenerative procedures based on tissue engineering.
The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals. In this paper, we develop a model-based ...classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram (EEG) signals. The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals. The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique. The model fitting was assessed using four statistical parameters, which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events. The proposed method was applied to 66 epochs from the Children Hospital Boston database. Results showed that the method achieved fast and accurate detection of epileptic seizures, with a 92% sensitivity, 96% specificity, and 94.1% accuracy.
We used an optimal control method involving covariant control equations as optimality conditions, to command the actuators of robot manipulators. These form a coupled system of second order nonlinear ...ordinary differential equations when associated with the robot motion equations. By solving this system, the control action required to take the robot from an initial to a final state is optimized in a prescribed time. However, the target set of equations exhibited stiffness. Therefore, an adequate solution could only be found for short trajectory durations with readily available numerical methods. We examined a time discretization procedure based on cubic and quintic Hermite finite elements which exhibited superconvergence properties for interpolation. This motivated us to develop a time integration algorithm based on Hermite's technique, where motion and control equations were perturbed to solve the optimal control problem. The optimal motion of a robotic manipulator was simulated using this algorithm. Our method was compared with a commercial differential equations solver on the basis of specific indicators. It outperformed the commercial solver by effectively solving the stiff set of equations for longer trajectory durations, with the cubic elements performing better than the quintic ones in this sense. The convergence analysis of our method confirmed that the quintic elements are more precise at the cost of increased computational burden, but converge at a lower rate than expected. Controlled motion experiments on a robotic manipulator validated our methodology. Trajectories were smoothly tracked and results exposed further methodology improvements.
This work presents a free new database designed from a real industrial process to recognize, identify, and classify the quality of the red raspberry accurately, automatically, and in real time. ...Raspberry trays with recently harvested fresh fruit enter the industry’s selection and quality control process to be categorized and subsequently their purchase price is determined. This selection is carried out from a sample of a complete batch to evaluate the quality of the raspberry. This database aims to solve one of the major problems in the industry: evaluating the largest amount of fruit possible and not a single sample. This major dataset enables researchers in various disciplines to develop practical machine-learning (ML) algorithms to improve red raspberry quality in the industry, by identifying different diseases and defects in the fruit, and by overcoming limitations by increasing the performance detection rate accuracy and reducing computation time. This database is made up of two packages and can be downloaded free from the Laboratory of Technological Research in Pattern Recognition repository at the Catholic University of the Maule. The RGB image package contains 286 raw original images with a resolution of 3948 × 2748 pixels from raspberry trays acquired during a typical process in the industry. Furthermore, the labeled images are available with the annotations for two diseases (86 albinism labels and 164 fungus rust labels) and two defects (115 over-ripeness labels, and 244 peduncle labels). The MATLAB code package contains three well-known ML methodological approaches, which can be used to classify and detect the quality of red raspberries. Two are statistical-based learning methods for feature extraction coupled with a conventional artificial neural network (ANN) as a classifier and detector. The first method uses four predictive learning from descriptive statistical measures, such as variance, standard deviation, mean, and median. The second method uses three predictive learning from a statistical model based on the generalized extreme value distribution parameters, such as location, scale, and shape. The third ML approach uses a convolution neural network based on a pre-trained fastest region approach (Faster R-CNN) that extracts its features directly from images to classify and detect fruit quality. The classification performance metric was assessed in terms of true and false positive rates, and accuracy. On average, for all types of raspberries studied, the following accuracies were achieved: Faster R-CNN 91.2%, descriptive statistics 81%, and generalized extreme value 84.5%. These performance metrics were compared to manual data annotations by industry quality control staff, accomplishing the parameters and standards of agribusiness. This work shows promising results, which can shed a new light on fruit quality standards methodologies in the industry.
This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: ...it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using a wavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4s, performing similarly to the best approaches from the literature.
Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming ...time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational complexity making it easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the continuous Morlet 1-D wavelet decomposition is computed. The generalized Gaussian distribution (GGD) is fitted to the resulting coefficients and their variance and median are calculated. Next, a k-nearest neighbors (k-NN) classifier is trained to detect the spike-and-wave patterns, using the scale parameter of the GGD in addition to the variance and the median. Experiments were conducted using EEG signals from six human patients. Precisely, 106 spike-and-wave and 106 non-spike-and-wave signals were used for training, and 96 other segments for testing. The proposed SWD classification method achieved 95% sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These promising results set the path for new research to study the causes underlying the so-called absence epilepsy in long-term EEG recordings.
Este artículo de investigación tiene por objeto analizar la seguridad social para las comunidades rurales en Colombia. Por un lado, presenta una descripción sobre el acceso a la seguridad social de ...las comunidades rurales conformadas por indígenas, afrodescendientes y campesinos, y por otro, un análisis sobre los pronunciamientos de la Corte Constitucional en torno a la consagración de la seguridad social como un derecho fundamental, universal e integral para todos los ciudadanos; por último, se conceptualiza sobre las acciones afirmativas, que pueden dirigir la actuación del Estado, en materia de seguridad social para las comunidades rurales. Así, a partir del análisis de datos y las fuentes documentales, fue posible concluir que nuestro sistema de seguridad social no diferencia entre campo y ciudad, ya que está estructurado a partir de la condición laboral de las personas, la capacidad de pago y los aportes al sistema, sin ofrecer alternativas o diseños de aseguramiento adecuados a las condiciones socio-económicas y de vida de las comunidades rurales.
Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the ...synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.
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