Directional dark field retrieval with single-grid x-ray imaging

Directional dark-field imaging is an emerging x-ray modality that is sensitive to unresolved anisotropic scattering from sub-pixel sample microstructures. A single-grid imaging set-up can be used to capture dark-field images by looking at changes in a grid pattern projected upon the sample. By creating analytical models for the experiment, we have developed a single-grid directional dark field retrieval algorithm that can extract dark-field parameters such as the dominant scattering direction, and the semi-major and -minor scattering angles. We show that this method is effective even in the presence of high image noise, allowing for low dose and time sequence imaging

Breast cancer classification with histopathological image based on machine learning

Breast cancer represents one of the most common reasons for death in the worldwide. It has a substantially higher death rate than other types of cancer. Early detection can enhance the chances of receiving proper treatment and survival. In order to address this problem, this work has provided a convolutional neural network (CNN) deep learning (DL) based model on the classification that may be used to differentiate breast cancer histopathology images as benign or malignant. Besides that, five different types of pre-trained CNN architectures have been used to investigate the performance of the model to solve this problem which are the residual neural network-50 (ResNet-50), visual geometry group-19 (VGG-19), Inception-V3, and AlexNet while the ResNet-50 is also functions as a feature extractor to retrieve information from images and passed them to machine learning algorithms, in this case, a random forest (RF) and k-nearest neighbors (KNN) are employed for classification. In this paper, experiments are done using the BreakHis public dataset. As a result, the ResNet-50 network has the highest test accuracy of 97% to classify breast cancer images

Teachers’ perception on the vitality of the English language among primary school students

In this multilingual milieu, Malaysians in their early age are given choices to use particular language in the various domains of usage. As such, it is rather common to have Malaysians speaking at least two if not more languages. Bringing the attention to English language, despite its status as the second most important language in the nation and is commonly known as the second language, English is gradually slanted towards a foreign language more than a second language (Platt & Weber, 1980; Nunan, 2003). Thus, it is of interest to examine the vitality of languages at the primary level of education as indicative of an early stage of language vitality. Although studies of language vitality are often linked to minority and indigenous languages, language vitality in the present study has broadened to encompass the main languages in the nation. Qualitative methodological approach was employed. Interviews were conducted with primary school teachers to obtain insights on the vitality issues. The study identified possible higher vitality of English language when students become older. It is seen that there are many folds of the vitality of English language and that it cannot be measured by scale alone. It is found that social milieu (in the sense of urban area versus rural area), socio-economic background and language policies contribute to the differences in the experience of the English language among students and thus influencing the vitality of the language

Teachers' perception on the vitality of the English language among primary school students

In this multilingual milieu, Malaysians in their early age are given choices to use particular language in the various domains of usage. As such, it is rather common to have Malaysians speaking at least two if not more languages. Bringing the attention to English language, despite its status as the second most important language in the nation and is commonly known as the second language, English is gradually slanted towards a foreign language more than a second language (Platt & Weber, 1980; Nunan, 2003). Thus, it is of interest to examine the vitality of languages at the primary level of education as indicative of an early stage of language vitality. Although studies of language vitality are often linked to minority and indigenous languages, language vitality in the present study has broadened to encompass the main languages in the nation. Qualitative methodological approach was employed. Interviews were conducted with primary school teachers to obtain insights on the vitality issues. The study identified possible higher vitality of English language when students become older. It is seen that there are many folds of the vitality of English language and that it cannot be measured by scale alone. It is found that social milieu (in the sense of urban area versus rural area), socio-economic background and language policies contribute to the differences in the experience of the English language among students and thus influencing the vitality of the language

A Review on Medicinal Properties of Orientin

Medicinal plants continue to play an important role in modern medications and healthcare as consumers generally believe that most of them cause fewer or milder adverse effects than the conventional modern medicines. In order to use the plants as a source of medicinal agents, the bioactive compounds are usually extracted from plants. Therefore, the extraction of bioactive compounds from medicinal plants is a crucial step in producing plant-derived drugs. One of the bioactive compounds isolable from medicinal plants, orientin, is often used in various bioactivity studies due to its extensive beneficial properties. The extraction of orientin in different medicinal plants and its medicinal properties, which include antioxidant, antiaging, antiviral, antibacterial, anti-inflammation, vasodilatation and cardioprotective, radiation protective, neuroprotective, antidepressant-like, antiadipogenesis, and antinociceptive effects, are discussed in detail in this review

Stacking Ensemble Approach for Churn Prediction: Integrating CNN and Machine Learning Models with CatBoost Meta-Learner

In the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple factors that exhibit nonlinear dependencies. In these complex scenarios, single classifiers may be unable to fully utilize the available information to capture the underlying interactions effectively. In contrast, ensemble learning that combines various base classifiers empowers a more thorough data analysis, leading to improved prediction performance. In this paper, a heterogeneous ensemble model is proposed for churn prediction in the telecom industry. The model involves exploratory data analysis, data pre-processing and data resampling to handle class imbalance. In this proposed model, multiple trained base classifiers with different characteristics are integrated through a stacking ensemble technique. Specifically, convolutional-based neural network, logistic regression, decision tree and Support Vector Machine (SVM) are considered as the base classifiers in this work. The proposed stacking ensemble model utilizes the unique strengths of each base classifier and leverages collective knowledge to improve prediction performance with a meta-learner. The efficacy of the proposed model is assessed on a real-world dataset, i.e., Cell2Cell. The empirical results demonstrate the superiority of the proposed model in churn prediction with 62.4% f1-score and 60.62% recall

The chromosomal DNA damage in buccal mucosa cells among schools children in the vicinity of mobile base stations in Selangor

Introduction: The increased use of mobile phones has increased the mobile base stations (MBS) deployment. While understanding of radiation protection is growing among the public, questions regarding early-life exposure to radiofrequency radiation (RFR) from MBS in children are of importance as to whether it will raise the chances of developing chronic diseases during adulthood. Taking into account the sitting location of MBS, the purpose of this study is to evaluate the chromosomal DNA damage in buccal mucosal cells between school children exposed to RFR emitted from base station antennas. Method: This is a comparative cross-sectional study in which two group of school children were sampled i.e. exposed groups are children whose school located near MBS (≤200 meters); unexposed groups are children whose school located distant far from the MBS (>200 meters). Digital RF Analyzer was used to measure RFR at the school surrounding. Buccal mucosa cells from the oral cavity were sampled to examine the level of micronuclei (MN) frequencies. Results: This study found that the densities of the RFR energy differed in range. Although all measurements showed the RFR reading below the acceptable exposure level, there were still significant variations at each location assessed. Statistically, the MN frequency is significantly different when compared to the exposed and non-exposed group. Conclusion: To understand the mechanism of health effects from exposure to low-level RFR emited from MBS, further study should consider environmental factors influencing MBS sitting on RFR emission, as well as examining the health effects into molecular levels

Genome-wide identification and expression analysis of banana RBOH genes in response to Fusarium oxysporum f. sp. cubense Tropical Race 4

Respiratory burst oxidase homologs (RBOH) is the key enzyme responsible for the production of reactive oxygen species which act as important signal during plant responses to abiotic and biotic stresses. However, RBOH homologs have not been characterized in banana. In this study, we have identified twelve Rboh genes distributed on eight chromosomes of Musa acuminata subsp. malaccensis (DH Pahang) through a genome-wide analysis. MaRbohs exist as sibling paralogs with variable exon-intron structures and highly conserved functional domain. Phylogenetic analysis clustered MaRBOH into four distinct subgroups (I, II, III and IV). The expression of MaRbohs following 24 hours of inoculation with Fusarium oxysporium f. sp. cubense Tropical Race 4 revealed diverse patterns in root tissues. MaRbohH1 and MaRbohA2 were strongly upregulated and downregulated, respectively in response to the root-dip inoculation. This is the first report on genome-wide characterization of Rboh genes from banana and their expression in response to the fungal pathogen TR4. This research provides a basis for exploration of the role of NADPH oxidase in banana defence against the root pathogen

Glucuronidation by UGT1A1 Is the Dominant Pathway of the Metabolic Disposition of Belinostat in Liver Cancer Patients

10.1371/journal.pone.0054522PLoS ONE81

A three gene DNA methylation biomarker accurately classifies early stage prostate cancer

Background: We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations. Materials and methods: We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real-time methylation-specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated. Results: In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S-transferase Pi 1 [GSTP1], CCDC181, hyaluronan, and proteoglycan link protein 3 [HAPLN3], GSTM2, growth arrest-specific 6 [GAS6], RASSF1, and APC) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation. Conclusion: We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three-gene biomarker represents a promising basis for more accurate prostate cancer diagnosis