Low-Dose CT Image Denoising using Image Decomposition and Sparse Representation

X-ray computed tomography (CT) is now a widely used imaging modality for numerous medical purposes. The risk of high X-ray radiation may induce genetic, cancerous and other diseases, demanding the development of new image processing methods that are able to enhance the quality of low-dose CT images. However, lowering the radiation dose increases the noise in acquired images and hence affects important diagnostic information. This paper contributes an efficient denoising method for low-dose CT images. A noisy image is decomposed into three component images of low, medium and high frequency bands; noise is mainly presented in the medium and high component images. Then, by exploiting the fact that a small image patch of the noisy image can be approximated by a linear combination of several elements in a given dictionary of noise-free image patches generated from noise-free images taken at nearly the same position with the noisy image, noise in these medium and high component images are effectively eliminated.Specifically, we give new solutions for image decomposition to easily control the filter parameters, for dictionary construction to improve the effectiveness and reduce the running-time. Instead of using a large dataset of patches, only a structured small part of patches extracted from the raw data is used to form a dictionary, to be used in sparse coding. In addition, we illustrate the effectiveness of the proposed method in preserving image details which are subtle but clinically important. Experimental results conducted on both synthetic and real noise data demonstrate that the proposed method is competitive with the state-of-the-art methods

Effects of plant essential oils and their constituents on Helicobacter pylori : A Review

Essential oils (EOs) obtained from different medicinal and aromatic plant families by steam distillation have been used in the pharmaceutical, food, and fragrance industries. The plant EOs and their broad diversity of chemical components have attracted researchers worldwide due to their human health benefits and antibacterial properties, especially their treatment of Helicobacter pylori infection. Since H. pylori has been known to be responsible for various gastric and duodenal diseases such as atrophic gastritis, peptic ulcer, gastric adenocarcinoma, and mucosa-associated lymphoid tissue lymphoma, several combination antibiotic therapies have been increasingly used to enhance the eradication rate of the bacterial infection. However, in the last decades, the efficacy of the therapies has decreased significantly due to widespread emergence of multidrug resistant strains of H. pylori. In addition, side-effects from commonly used antibiotics and recurrence of the bacterial infection have drawn public health concern globally.Therefore, this review focuses on in vitro effects of plant EOs and their bioactive constituents on the growth, cell morphology and integrity, biofilm formation, motility, adhesion, and urease activity of H. pylori. Their inhibitory effects on expression of genes necessary for growth and virulence factor productions of the bacterial pathogen are also discussed. Further in vivo and clinical evaluations are required so that plant EOs and their bioactive constituents can be possibly applicable in pharmacy or as adjuvants to the current therapies of H. pylori infection

New records and morphological assessments of long-nosed fruit bats (chiroptera: pteropodidae: Macroglossus spp.) from Vietnam

Long-nosed fruit bat is a common name of the genus Macroglossus which comprises two species: Dagger-toothed long-nosed fruit bat (M. minimus) and Greater long-nosed fruit bat (M. sobrinus). These two species were rarely recorded from Vietnam or neighboring countries. Within Vietnam, M. minimus has been recorded only from two localities in southern Vietnam while M. sobrinus was known from all northern, central and southern regions of the country. Morphological features of these species in Vietnam were poorly documented in previous publications. With results from a rapid examination of all specimens and recently captured individuals, we here confirm that M. sobrinus is distinctively larger than M. minimus in all external and craniodental measurements. Two species are also distinguishable by their nostril shapes and mandible symphyses. This paper provides new distributional records of both M. sobrinus and M. minimus from Vietnam with remarks on their ecology and habitats.

New records of bats (Mammalia: Chiroptera) from Cu Lao Cham and Ly Son archipelagos, central Vietnam

Cu Lao Cham and Ly Son are two well-known archipelagos of Vietnam for their specular landscapes and varied ecosystems including forest, cave, and agriculture. However, their bat fauna has received little attention. Between July 2017 and August 2018, we conducted a series of mammal surveys with emphasis on bats of the two archipelagos. Bats were captured by mist nets and harp traps. Echolocation calls of microchiropteran species were recorded using the PCTape system then analysed by Selena software. With reference to all available literatures and specimens from the recent surveys, we obtained confirmed records of 9 bat species from Cu Lao Cham and 3 species from Ly Son. Of these, Megaderma spasma and Taphozous melanopogon are new to Cu Lao Cham while Rhinolophus macrotis is new to Ly Son. These three species were rarely recorded from other islands of Vietnam and also uncommon within Cu Lao Cham and Ly Son. These new records not only expand the known distributional range, but also provide worthwhile notes on a narrow geographical variation in morphology and echolocation of each species

DEFEG: deep ensemble with weighted feature generation.

With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcForest that involves several layers of Random Forest-based classifiers. Although gcForest has outperformed several benchmark algorithms on specific datasets in terms of classification accuracy and model complexity, its input features do not ensure better performance when going deeply through layer-by-layer architecture. We address this limitation by introducing a deep ensemble model with a novel feature generation module. Unlike gcForest where the original features are concatenated to the outputs of classifiers to generate the input features for the subsequent layer, we integrate weights on the classifiers’ outputs as augmented features to grow the deep model. The usage of weights in the feature generation process can adjust the input data of each layer, leading the better results for the deep model. We encode the weights using variable-length encoding and develop a variable-length Particle Swarm Optimisation method to search for the optimal values of the weights by maximizing the classification accuracy on the validation data. Experiments on a number of UCI datasets confirm the benefit of the proposed method compared to some well-known benchmark algorithms

Antibacterial activity of Piper betle extracts on Helicobacter pylori and identification of potential compounds

Helicobacter pylori is one of the most common infectious bacteria in the world that causes gastric diseases leading to cancer. The increase of multiple antibiotic resistance rates of H. pylori have been reported worldwide. Thus, development of novel drugs is urgently required. Piper betle has many therapeutic values in traditional medicine. In this study, therefore, we investigated antibacterial activity of P. betle extracts and their fractions against a H. pylori strain isolated in Vietnam. The agar disk diffusion assay showed inhibition zone of ethyl acetate extract and methanol extract from P. betle leaf that of were 46 mm and 32 mm in diameter, respectively. After fractionation of the ethyl acetate extract through silica gel column chromatography, two peaks, PD2 and PD3, out of 12 fractions showed the strongest antibacterial activity. PD2 was sub-fractionated further by re-chromatography on the silica gel column, and subfraction TK12 gave best resolution on LC-MS analysis. Finally, 4 potential compounds, quercetrin, calodenin B, vitexin and plicatipyrone, were identified in TK12 fraction.

TYPES OF POLITENESS STRATEGIES AND DEGREES OF POLITENESS PERFORMED BY ENGLISH MAJOR STUDENTS IN REQUESTING FOR HELP

In social interaction, people need to pay attention to the face of others to maintain relationships and avoid losing their faces. To do this, people should use politeness strategies in communication. This study aims to investigate which type of politeness strategies are mostly used and the level of politeness shown by English major students in the High Quality Program in requesting help. This study is based on the theory of Brown and Levinson (1987). Based on the analysis of the data obtained from the questionnaire, negative politeness strategies were applied the most. This also performs a high degree of politeness. It shows that students majoring in English studies (High Quality Program) had an awareness of using politeness strategies in requesting help.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0704/a.php" alt="Hit counter" /></p&gt

A homogeneous-heterogeneous ensemble of classifiers.

In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each other after using random projections, we construct the homogeneous module by applying random projections on the training data to obtain the new training sets. In the heterogeneous module, several learning algorithms will train on the new training sets to generate the base classifiers. We propose four combining algorithms based on Sum Rule and Majority Vote Rule for the proposed ensemble. Experiments on some popular datasets confirm that the proposed ensemble method is better than several well-known benchmark algorithms proposed framework has great flexibility when applied to real-world applications. The proposed framework has great flexibility when applied to real-world applications by using any techniques that make rich training data for the homogeneous module, as well as using any set of learning algorithms for the heterogeneous module

A moving element method using timoshenko’s beam theory for dynamic analysis of train-track systems

The paper presents a dynamic analysis of train-track systems supported by viscoelastic foundations by combining Timoshenko’s beam theory and moving element method (MEM). In the proposed method, a three-node beam element is utilized to get a high order approximation for the deflection of Timoshenko beam. The reduced integral method is applied in order to avoid the shear-locking phenomenon when computing the shear strain energy of the rail beam. In addition, the behavior of train-track system with respect to time is deduced by using Newmark’s constant acceleration method. Numerical results show that the proposed method is free of shear locking and gives a good agreement with Koh et al.’s method using Euler-Bernoulli beam theory

Adsorption of Co(II) from the simulated solution by zeolite NaX derived from rice husk ash

The adsorption of Co(II) from the simulated solution was investigated using zeolite NaX derived from rice husk ash as an alternative adsorbent. The adsorption behavior of Co(II) depended strongly on the equilibrium pH, Co (II) concentration, zeolite NaX dosage, and reaction time. The high adsorption efficiency of Co(II) by zeolite NaX was obtained under the conditions: pH 3.0, 100 mg/L of Co(II), 5 g/L of zeolite NaX, and a reaction time of 75 min. The loading behavior of Co(II) onto the zeolite NaX was well-fitted to the Freundlich adsorption isotherm and the Co(II) loading capacity by zeolite NaX was around 38 mg/g. The obtained results indicate that synthesized zeolite NaX from rice husk ash is a potential adsorbent to remove cobalt from waste solutions due to its high adsorption