Data mining approach in prediction of Erythropoietin dosage in Hemodialysis patients

Background and purpose: Kidney failure reduces the kidney function and in long term it leads to chronic kidney disease. One of the main complications of this disease is irreversible damage to the kidneys (end-stage kidney disease) and hemodialysis is the main method used to treat advanced kidney failure. The main problem associated with hemodialysis is treating anemia caused by lack of erythropoietin secretion in kidney which is usually treated by synthetic erythropoietin. On the other hand, choosing the right dosage of erythropoietin is important because it is expensive and could have some complications. This research aimed at predicting the dosage of erythropoietin and identifying affecting factors. Materials and methods: Data was collected from a dialysis center in Tehran and data mining methods were used. The input variables were measured in the past 6 months of treating patients with erythropoietin. The sequential data was then converted to the bag of features (BOF) format. Then support vector machines and random forest were applied on the BOF to predict the erythropoietin dosage. Results: The amount of medication in previous months was found to be an important factor in determining the appropriate dosage of erythropoietin for the next month. In optimal condition, random forest and SVM could predict the erythropoietin dosage with an average accuracy of 90 and 79, respectively. Conclusion: This study identified the factors influencing the treatment and control of anemia in hemodialysis patients. These results could be of great benefit in prescribing the proper dosage of erythropoietin, and reducing the treatment cost and duration. Moreover, it helps to prevent the complications caused by excessive use of erythropoietin such as increase in hemoglobin level. © 2015, Mazandaran University of Medical Sciences. All rights reserved

IMPACT OF TEACHERS’ PROFESSIONAL SKILLS ON STUDENT ACHIEVEMENT WHILE LEARNING ENGLISH AS A SECOND LANGUAGE IN SRI LANKA

The public education system of Sri Lanka suffers from poor education outcomes and wide disparities in academic achievement across schools, and studies have revealed that students’ outcomes are best when the teachers are qualified in both, the subjects they are assigned to teach and in pedagogy. Though the studies have revealed that student outcomes can be improved by improving the professional skills of teachers, any convincing evidence of the influence of teachers’ professional skills on student outcomes in Sri Lanka cannot be found. To fulfil that gap, this study explored the influence of teachers’ professional skills on student outcomes in government schools in Sri Lanka, using the pretest, posttest experimental design. Data were collected before and after the 8-week intervention period, from a sample consisting of 102 students and four teachers of English, that were observed quantitatively. Findings revealed that teachers’ professional skills as one of the most influential factors that decide the student outcomes directly of a school, which offer valuable recommendations to the education system in Sri Lanka, in order to enhance student outcomes by taking steps to improve the professional skills of teachers.  Article visualizations

Accelerated mechanical fatigue testing and lifetime of interconnects in microelectronics

AbstractDue to the rapid development of packaging industry accelerated reliability testing for evaluation of lifetime of electronic components are increasingly utilized. In addition to common active thermal cycling procedures, accelerated mechanical fatigue testing provides a new possibility to assess the reliability of microelectronic components, mainly due to the significantly shorter duration of testing time. In this investigation we have used an ultrasonic fatigue testing system in combination with a special experimental set-up for qualification and lifetime determination of microelectronic interconnects. Using this technique, fatigue life of Al wire bonded interconnects were determined and S-N curves (shear stress as a function of loading cycles) up to N=109 were plotted. Three dimensional elasto-plastic FEM simulations were performed to determine the distributions of shear stress and plastic strain generated during cyclic fatigue in the bond area. Furthermore, the FEM model was employed to predict the lifetime of wire-bonds. The results were correlated to the lifetime curves of similar bonds obtained by power cycling tests. Detailed microstructural investigations were performed by means of EBSD -SEM to study the evolution of microstructure of the interconnects subjected to thermal and mechanical fatigue loading. This study demonstrates the applicability of accelerated mechanical fatigue testing as an alternative to time consuming thermal cycling for qualification of microelectronic interconnects

IMPACT OF TEACHERS' MINDFULNESS AND PROFESSIONAL SKILLS TOWARDS CLASSROOM CLIMATE ON STUDENT OUTCOMES WHILE LEARNING ENGLISH AS A SECOND LANGUAGE IN SRI LANKA

Public education system of Sri Lanka suffers from poor education outcomes and wide disparities in academic achievement across schools, and studies reveal that students’ outcomes are best when the teachers are qualified in both subjects, they are assigned to teach and in pedagogy. At present teacher stress has become a critical issue in the educational field which affects negatively towards classroom climate; teacher student relationship, teacher creativity, teaching methodologies and student outcomes, while a positive classroom climate results in better academic, social, and emotional outcomes for students. Though the studies have revealed that teacher can improve their well-being, as well as the classroom climate and the student outcomes through the use of mindfulness practices which was taught by the Buddha 2600 years ago, any convincing evidence of the influence of both, teachers’ mindfulness and professional skills on classroom climate towards student outcomes in Sri Lanka or any other country cannot be found. To fulfil that gap, this study explored the influence of teachers’ mindfulness and professional skills on classroom climate towards student outcomes in the government schools in Sri Lanka, using pre-test and post-test experimental design. Data was collected before and after 8-week intervention period, from a sample consisted of 230 students and nine teachers of English, that were observed quantitatively. Findings revealed that teachers’ mindfulness and professional skills as two of the most influential factors that decide the student outcomes directly and via classroom climate of a school, that offer valuable recommendations to the education system in Sri Lanka, in order to enhance student academic and behavioural outcomes by taking steps to improve the professional skills of teachers and the mindful living of both teachers and students.  Article visualizations

Incorporating family function into chronic pain disability: The role of catastrophizing

Background: Observers' responses to pain are recently investigated to more comprehensively explain chronic pain (CP) and disability. However, the role of family context, defined as interference in roles, communication, and problem-solving, and how (i.e., through which mechanisms) these variables contribute to CP related disability have yet to be examined. Objectives: The aim of the present study is to examine family context in relationship to pain catastrophizing, fear of movement, and depression and its role in understanding CP disability. Three different models were examined. Methods: A total sample of 142 patients with musculoskeletal chronic pain was recruited to examine the role of fear of movement, pain intensity, pain catastrophizing, and depression in relationship to family functioning as predictors of disability. Results: Findings indicated that two models showed acceptable fit, but one of them revealed superior fit indices. Results of the model with superior fit indices indicated that family dysfunction may contribute to catastrophic thinking, which, in turn, contributes to patients' disability through increasing fear of movement and depression. Discussion.The current study provides further support for the notion that the impact of emotional and cognitive variables upon CP-related disability can be better understood when we consider the social context of pain patients and family function in particular. Copyright © 2016 Fatemeh Akbari et al

Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study

Background: Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features. Method: A two-step stack ensemble classifier is proposed for classifying the instances into stillbirth and livebirth at the first step and then, classifying stillbirth before delivery from stillbirth during the labor at the second step. The proposed SE has two consecutive layers including the same classifiers. The base classifiers in each layer are decision tree, Gradient boosting classifier, logistics regression, random forest and support vector machines which are trained independently and aggregated based on Vote boosting method. Moreover, a new feature ranking method is proposed in this study based on mean decrease accuracy, Gini Index and model coefficients to find high-ranked features. Results: IMAN registry dataset is used in this study considering all births at or beyond 28th gestational week from 2016/04/01 to 2017/01/01 including 1,415,623 live birth and 5502 stillbirth cases. A combination of maternal demographic features, clinical history, fetal properties, delivery descriptors, environmental features, healthcare service provider descriptors and socio-demographic features are considered. The experimental results show that our proposed SE outperforms the compared classifiers with the average accuracy of 90, sensitivity of 91, specificity of 88. The discrimination of the proposed SE is assessed and the average AUC of ±95, CI of 90.51 ±1.08 and 90 ±1.12 is obtained on training dataset for model development and test dataset for external validation, respectively. The proposed SE is calibrated using isotopic nonparametric calibration method with the score of 0.07. The process is repeated 10,000 times and AUC of SE classifiers using random different training datasets as null distribution. The obtained p-value to assess the specificity of the proposed SE is 0.0126 which shows the significance of the proposed SE. Conclusions: Gestational age and fetal height are two most important features for discriminating livebirth from stillbirth. Moreover, hospital, province, delivery main cause, perinatal abnormality, miscarriage number and maternal age are the most important features for classifying stillbirth before and during delivery. © 2021, The Author(s)

An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs

Background: Transcription factors (TFs) control transcription by binding to specific regions of DNA called transcription factor binding sites (TFBSs). The identification of TFBSs is a crucial problem in computational biology and includes the subtask of predicting the location of known TFBS motifs in a given DNA sequence. It has previously been shown that, when scoring matches to known TFBS motifs, interdependencies between positions within a motif should be taken into account. However, this remains a challenging task owing to the fact that sequences similar to those of known TFBSs can occur by chance with a relatively high frequency. Here we present a new method for matching sequences to TFBS motifs based on intuitionistic fuzzy sets (IFS) theory, an approach that has been shown to be particularly appropriate for tackling problems that embody a high degree of uncertainty. Results: We propose SCintuit, a new scoring method for measuring sequence-motif affinity based on IFS theory. Unlike existing methods that consider dependencies between positions, SCintuit is designed to prevent overestimation of less conserved positions of TFBSs. For a given pair of bases, SCintuit is computed not only as a function of their combined probability of occurrence, but also taking into account the individual importance of each single base at its corresponding position. We used SCintuit to identify known TFBSs in DNA sequences. Our method provides excellent results when dealing with both synthetic and real data, outperforming the sensitivity and the specificity of two existing methods in all the experiments we performed. Conclusions: The results show that SCintuit improves the prediction quality for TFs of the existing approaches without compromising sensitivity. In addition, we show how SCintuit can be successfully applied to real research problems. In this study the reliability of the IFS theory for motif discovery tasks is proven

The aesthetics and politics of ‘reading together’ Moroccan novels in Arabic and French

This paper attempts to break down the common practices of reading multilingual Moroccan novels, particularly Moroccan postcolonial novels in Arabic and French. I argue that dominant reading practices are based on binary oppositions marked by a reductionist understanding of language and cultural politics in Morocco. They place the Moroccan novel in Arabic and French in independent traditions with the presupposition that they have no impact on each other, thereby reifying each tradition. They also ignore the similar historical, social and cultural context from which these novels emerge, and tend to reinforce the marginalisation of the Moroccan novel within hegemonic single-language literary systems such as the Francophone or Arabic literary traditions. I advocate ‘reading together’ – or an entangled comparative reading of – postcolonial Moroccan novels in Arabic and French, a reading that privileges the specificity of the literary traditions in Morocco rather than language categorisation, and that considers their mutual historical, cultural, geographical, political, and aesthetic interweaving and implications