Teacher Effectiveness and Student Achievement in the Smart School Hyderabad

The study aimed to identify the role of effective teacher on student’s achievement in school, how teachers teaching can be effective and what are the qualities of effective teachers and how it can affect students learning. This study was conducted in Virtual university of Pakistan in Hyderabad campus, total of 266 respondents were selected to collect data from the school “ the smart school” Hyderabad from three different campuses of the school randomly from class 7, 8, 9 and 10 The main research of the study was on the teacher’s effective teaching and its effect of student’s achievements, the researcher used 5 points grading questionnaire paper in which 1: strongly disagree, 2: disagree, 3: neutral, 4: agree and 5: strongly agree was used in order to know the relationship between the variables. This research highlights the main theories and facts of effective teachers teaching, what teachers should adopt, how teachers can influence and keep students motivated towards learning and what are the effect of effective teachers on students' achievement, in their learning and grades

Lyme rashes disease classification using deep feature fusion technique

Automatic classification of Lyme disease rashes on the skin helps clinicians and dermatologists’ probe and investigate Lyme skin rashes effectively. This paper proposes a new in-depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state-of-the-art models.© 2023 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

A statistical model for improved membrane protein expression using sequence-derived features

The heterologous expression of integral membrane proteins (IMPs) remains a major bottleneck in the characterization of this important protein class. IMP expression levels are currently unpredictable, which renders the pursuit of IMPs for structural and biophysical characterization challenging and inefficient. Experimental evidence demonstrates that changes within the nucleotide or amino-acid sequence for a given IMP can dramatically affect expression levels; yet these observations have not resulted in generalizable approaches to improve expression levels. Here, we develop a data-driven statistical predictor named IMProve, that, using only sequence information, increases the likelihood of selecting an IMP that expresses in E. coli. The IMProve model, trained on experimental data, combines a set of sequence-derived features resulting in an IMProve score, where higher values have a higher probability of success. The model is rigorously validated against a variety of independent datasets that contain a wide range of experimental outcomes from various IMP expression trials. The results demonstrate that use of the model can more than double the number of successfully expressed targets at any experimental scale. IMProve can immediately be used to identify favorable targets for characterization. Most notably, IMProve demonstrates for the first time that IMP expression levels can be predicted directly from sequence

Decoding sequence-level information to predict membrane protein expression

The expression and purification of integral membrane proteins remains a major bottleneck in the characterization of these important proteins. Expression levels are currently unpredictable, which renders the pursuit of these targets challenging and highly inefficient. Evidence demonstrates that small changes in the nucleotide or amino-acid sequence can dramatically affect membrane protein biogenesis; yet these observations have not resulted in generalizable approaches to improve expression. In this study, we develop a data-driven statistical model that predicts membrane protein expression in E. coli directly from sequence. The model, trained on experimental data, combines a set of sequence-derived variables resulting in a score that predicts the likelihood of expression. We test the model against various independent datasets from the literature that contain a variety of scales and experimental outcomes demonstrating that the model significantly enriches expressed proteins. The model is then used to score expression for membrane proteomes and protein families highlighting areas where the model excels. Surprisingly, analysis of the underlying features reveals an importance in nucleotide sequence-derived parameters for expression. This computational model, as illustrated here, can immediately be used to identify favorable targets for characterization

A statistical model for improved membrane protein expression using sequence-derived features

The heterologous expression of integral membrane proteins (IMPs) remains a major bottleneck in the characterization of this important protein class. IMP expression levels are currently unpredictable, which renders the pursuit of IMPs for structural and biophysical characterization challenging and inefficient. Experimental evidence demonstrates that changes within the nucleotide or amino-acid sequence for a given IMP can dramatically affect expression levels; yet these observations have not resulted in generalizable approaches to improve expression levels. Here, we develop a data-driven statistical predictor named IMProve, that, using only sequence information, increases the likelihood of selecting an IMP that expresses in E. coli. The IMProve model, trained on experimental data, combines a set of sequence-derived features resulting in an IMProve score, where higher values have a higher probability of success. The model is rigorously validated against a variety of independent datasets that contain a wide range of experimental outcomes from various IMP expression trials. The results demonstrate that use of the model can more than double the number of successfully expressed targets at any experimental scale. IMProve can immediately be used to identify favorable targets for characterization. Most notably, IMProve demonstrates for the first time that IMP expression levels can be predicted directly from sequence

Does A Human Resource Effect of Job Satisfaction of Teachers? A Case Study of Education Institutions in Punjab, Pakistan

The aim of this paper is to find the impact of pay & promotion, work load a teacher’s job satisfaction. This study was used SPSS and SMART-PLS to analyze a data using quantitative research method. The research to which have distributed the question among teachers, graduates and a different professional. This study is significant for universities, colleges and schools, academic and non-academic staffs. Because this study have helpful for the administration in institute to better understand needs and demands of their teachers and what will be the factors which could make them satisfied. This study have conducted in Multan, Khanewal, Vehari, Bahawalpur G.D khan and it is targeting to cover approximately 7721 population with the sample of 350 as defined by Sekaran (2012) that for 7000 to 8000 required samples sized is 372. This research also examines the influence of compensation and promotion upon job satisfaction at educational institution level. The reveals that pay and promotion not significant impact on job satisfaction of teacher. (Yee 2018) investigate in his study that pay and promotion are insignificant. Work load can also be significant association with job satisfaction of teachers. Raza et al (2015) investigate that work load significant impact on job satisfaction. The influence of these factors calls for the further research. There is also need to carry out a similar but comparative study in rural setting

Chronic obstructive pulmonary disease and associated healthcare resource consumption in the Middle East and North Africa: The BREATHE study

SummaryData on COPD-related healthcare resources use are rarely documented in developing countries. This article presents data on COPD-related healthcare resource consumption in the Middle East, North Africa and Pakistan and addresses the association of this variable with illness severity. A large survey of COPD was conducted in eleven countries of the region, namely Algeria, Egypt, Jordan, Lebanon, Morocco, Pakistan, Saudi-Arabia, Syria, Tunisia, Turkey and United Arab Emirates, using a standardised methodology. A total of 62,086 subjects were screened. This identified 2,187 subjects fulfilling the “epidemiological” definition of COPD. A detailed questionnaire was administered to document data on COPD-related healthcare consumption. Symptom severity was assessed using the COPD Assessment Test (CAT). 1,392 subjects were analysable. Physician consultations were the most frequently used healthcare resource, ranging from 43,118 [95% CI: 755–85,548] consultations in UAE to 4,276,800 [95% CI: 2,320,164–6,230,763] in Pakistan, followed by emergency room visits, ranging from 15,917 [95% CI: 0–34,807] visits in UAE to 683,697 [95% CI: 496,993–869,737] in Turkey and hospitalisations, ranging from 15,563 [95% CI: 7,911–23,215] in UAE to 476,674 [95% CI: 301,258–652,090] in Turkey. The use of each resource increased proportionally with the GOLD 2011 severity groups and was significantly (p < 0.0001) higher in subjects with more symptoms compared to those with lower symptoms and in subjects with exacerbations to those without exacerbations. The occurrence of exacerbations and the CAT score were independently associated with use of each healthcare resource. In conclusion, the BREATHE study revealed that physician consultation is the most frequently COPD-related healthcare resource used in the region. It showed that the deterioration of COPD symptoms and the frequency of exacerbations raised healthcare resource consumption

Photocatalytic degradation of organic dyes and biological potentials of biogenic zinc oxide nanoparticles synthesized using the polar extract of Cyperus scariosus R.Br. (Cyperaceae)

In this study, the polar root extract of Cyperus scariosus R.Br. was used for the biogenic synthesis of ZnO NPs. The results of this study show that ZnO NPs have a spherical structure with an average size of 85.4 nm. The synthesized catalysts were tested for their photocatalytic activity by degrading methyl orange and methylene blue under sunlight. Improved degradation efficiencies of 79.44% and 84.92% were achieved within 120 min. ZnO NPs exhibited strong antibacterial activity against both Gram-positive Listeria monocytogenes (18 mm) and Staphylococcus epidermidis (20 mm) and Gram-negative strains of Escherichia coli (16 mm) and Bordetella bronchiseptica (14 mm), as shown by the inhibition zones, which were comparable to the positive control (ceftriaxone) but larger than the plant root extract. ZnO NPs showed high antioxidant activity, as a ferric-reducing antioxidant power assay value of 66.29 µg (AAE µg·mL−1) and a DPPH value of 57.44 µg (AAE µg·mL−1) were obtained at a concentration of 500 µL, which was higher than those of the C. scariosus root extract. Quantification of the total phenolic and flavonoid content yielded values of 57.63 µg (GAE µg·mL−1) and 70.59 µg (QCE µg·mL−1), respectively. At a concentration of 500 μL (1 mg·mL−1), the tested nanoparticles (NPs) showed a greater anti-inflammatory effect (84.12%) compared to the root extract of C. scariosus (34.39%). Overall, our findings highlight the versatile properties of green synthesized ZnO NPs and demonstrate their potential for environmental remediation and antimicrobial formulations, as well as promising candidates for further investigation in biomedical fields such as drug delivery and therapy