Cinchona officinalis L. is one of the most important and historically medicinal plants from which the antimalarial drug known as quinine is extracted. It is currently an endangered species. Thus, in vitro culture techniques are applied to propagate the species and to evaluate the effect of artificial light on the physiological development of C. Officinalis L. under controlled conditions. In that sense, the current study has determined the impact of blue led light on the enhancement of growth and number of shoots of Cinchona officinalis L. In vitro explants of C. Officinalis L were cultured on Murashige and Skoog (MS) medium and cultured under the white (control) and blue light-emitting diodes (LED) light. After eight weeks, growth and bud numbers were determined in C. officinalis L. Interestingly, blue light treatment increased the shoot length and bud numbers in comparison with the control. Incorporating blue light during in vitro propagation of C. Officinalis L can be a beneficial way to increase plant quality. Future perspectives could include the impact of blue light on the production of secondary metabolites, activities of antioxidant enzymes, and protein expression of in vitro-grown C. Officinalis L.
KEYWORDS: Image segmentation, Digital filtering, Image filtering, Gaussian filters, Mammography, Image quality, Denoising, Breast cancer, Image processing algorithms and systems
Digital mammography is a valuable technique for breast cancer detection, because it is safe, noninvasive and can reduce unnecessary biopsies. However, it is difficult to distinguish masses from normal or dense regions because of their morphological characteristics and ambiguous margins. Thus, improvement of image quality, highlighting the tissues details and performing mass segmentation are important tasks for early breast cancer diagnosis. This work presents a mini-Mammographic Image Analysis Society (MIAS) database preprocessing, system which combines classic and efficient techniques of Median, Wiener and Gaussian filters to remove salt and pepper, speckle and gaussian noise in mammography images. The experimental results indicates that the Gaussian filter outperforms other filtering techniques, as shown by evaluated by Peak Signal to Noise Ratio and Mean Square Error metrics.
Epilepsy is a chronic neurological disorder that causes unprovoked and recurrent seizures which according to WHO affects approximately 50 million people worldwide. Functional magnetic resonance images (MRI) help to identify certain affected areas of the brain, namely, the gliosis and hippocampal volume loss. These losses cause complex epilepsy, and is known as hippocampal sclerosis or Mesial Temporal Sclerosis (MTS). This work presents the development of a Computer Aided Diagnosis CAD system software package) that can be used to identify the characteristics and patterns of MTS from brain magnetic resonance images. The image processing techniques involve texture analysis, statistical features, evaluation of the 3D Region of interest (ROI), and threshold analysis. The software allows the automatic evaluation of the degeneration of hippocampal structures, hippocampal volume and signal intensity. We will describe and demonstrate the software (which can currently be accessed on GitHub). It is expected that this tool will be useful in new neurology/radiology specialists and can serve as a secondary diagnosis. However, it is necessary to validate the software system qualitatively and quantitatively in order to get more effectiveness and efficiency in a real-world clinical application.
Mesial temporal sclerosis (MTS) is the principal cause of complex epilepsy, is manifested principally by gliosis and hippocampal volume loss. This project aims to develop an algorithm that allows automatic measurement of hippocampal volume and signal intensity in magnetic resonance imaging. The algorithm developed uses preprocessing of the images to reduce the artifacts and for the extraction of the features were used techniques of machine learning (support vector machine) and texture analysis. Results can help to optimize time in the assessment of the mesial temporal sclerosis and can contribute to the best training to the youngers neuroradiologists.
Brain demyelination lesions occur due to damage of the myelin layer of nerve fibers, this deterioration is the cause of pathologies such as multiple sclerosis, leukodystrophy, encephalomyelitis. Brain ischemia is the interruption of the blood supply to the brain, and the flow of oxygen and nutrients needed to maintain the correct functioning of brain cells. This project presents the results of an algorithm processing images with the the main objective of identify and differentiate between demyelination and ischemic brain diseases through the automatic detection, classification and identification of their features found in the magnetic resonance images. The sequences of images used were T1, T2, and FLAIR and with a dataset of 300 patients with and without these or other pathologies, respectively. The algorithm in this stage uses Discrete Wavelet Transform (DWT), principal component analysis (PCA) and a kernel support vector machine (SVM). The algorithm developed indicates a 75% of accuracy, for that reason, with an effective validation could be applied for the fast diagnosis and contribute to an effective treatment of these brain diseases especially in the rural places.
The effect of laser irradiation is one of the most important factors that affect the bacteria survival due to the wavelengths that emit the different light sources. The high-intensity broadband visible light (400–800nm) can reduce viability of bacterial strains. The main objective is to assess the most effective wavelengths of visible light in growth of four beneficial rhizobacteria. The survival of bacterial cells following illumination was monitored by optical density after exposure of the suspended bacteria to light at different time of incubation. Bacterial grow under the same conditions but without light exposure as controls. The visible light with wavelength between 450-590nm increase the bacterial growth in vitro conditions.
This work presents the advance to development of an algorithm for automatic detection of demyelinating lesions and cerebral ischemia through magnetic resonance images, which have contributed in paramount importance in the diagnosis of brain diseases. The sequences of images to be used are T1, T2, and FLAIR.
Brain demyelination lesions occur due to damage of the myelin layer of nerve fibers; and therefore this deterioration is the cause of serious pathologies such as multiple sclerosis (MS), leukodystrophy, disseminated acute encephalomyelitis. Cerebral or cerebrovascular ischemia is the interruption of the blood supply to the brain, thus interrupting; the flow of oxygen and nutrients needed to maintain the functioning of brain cells. The algorithm allows the differentiation between these lesions.
The present work shows the teaching and motivation of University students to think about optics and color effects. The methodology consists of studying the different optical phenomena that occur through the sunsets and then do a correlation of this information with the phenomena and optical effects of the color of class presentations; to determine the motivation and attention of students.
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