M-health has changed the conventional delivery system of health-care, permitting continuous, pervasive Health-care anywhere, anytime. Chronic disease apps are increasing, as many health workers, patients and clinicians already embracing smartphones in their comprehensive and diverse practices. There are lots of challenges and requirements that need to be addressed for mobile health applications to prevent or eliminate design problems and minimize potential threats for users, the proposed factors for chronic disease mobile applications can be used as a guide for app developers While, the usability testing, and evaluations of chronic disease apps have not yet touched the accuracy level of other web based applications. This study is being conducted to learn about challenges of m-health apps and to identify the factors that affect the usability of such applications.

Arsenic (As) is a well-known toxic metalloid found naturally and released by different industries, especially in developing countries. Purple nonsulfur bacteria (PNSB) are known for wastewater treatment and plant growth promoting abilities. As-resistant PNSB were isolated from a fish pond. Based on As-resistance and plant growth promoting attributes, 2 isolates CS2 and SS5 were selected and identified as Rhodopseudomonas palustris and Rhodopseudomonas faecalis, respectively, through 16S rRNA gene sequencing. Maximum As(V) resistance shown by R. faecalis SS5 and R. palustris CS2 was up to 150 and 100 mM, respectively. R. palustris CS2 showed highest As(V) reduction up to 62.9% (6.29 ± 0.24 mM), while R. faecalis SS5 showed maximum As(III) oxidation up to 96% (4.8 ± 0.32 mM), respectively. Highest auxin production was observed by R. palustris CS2 and R. faecalis SS, up to 77.18 ± 3.7 and 76.67 ± 2.8 μg mL−1, respectively. Effects of these PNSB were tested on the growth of Vigna mungo plants. A statistically significant increase in growth was observed in plants inoculated with isolates compared to uninoculated plants, both in presence and in absence of As. R. palustris CS2 treated plants showed 17% (28.1 ± 0.87 cm) increase in shoot length and 21.7% (7.07 ± 0.42 cm) increase in root length, whereas R. faecalis SS5 treated plants showed 12.8% (27.09 ± 0.81 cm) increase in shoot length and 18.8% (6.9 ± 0.34 cm) increase in root length as compared to the control plants. In presence of As, R. palustris CS2 increased shoot length up to 26.3% (21.0 ± 1.1 cm), while root length increased up to 31.3% (5.3 ± 0.4 cm), whereas R. faecalis SS5 inoculated plants showed 25% (20.7 ± 1.4 cm) increase in shoot length and 33.3% (5.4 ± 0.65 cm) increase in root length as compared to the control plants. Bacteria with such diverse abilities could be ideal for plant growth promotion in As-contaminated sites.

Introduction: Machine Learning (ML) is a rapidly growing subfield of Artificial Intelligence (AI). It is used for different purposes in our daily life such as face recognition, speech recognition, text translation in different languages, weather prediction, and business prediction. In parallel, ML also plays an important role in the medical domain such as in medical imaging. ML has various algorithms that need to be trained with large volumes of data to produce a well-trained model for prediction. Aim: The aim of this study is to highlight the most suitable Data Augmentation (DA) technique(s) for medical imaging based on their results. Methods: DA refers to different approaches that are used to increase the size of datasets. In this study, eight DA approaches were used on publicly available low-grade glioma tumor datasets obtained from the Tumor Cancer Imaging Archive (TCIA) repository. The dataset included 1961 MRI brain scan images of low-grade glioma patients. You Only Look Once (YOLO) version 3 model was trained on the original dataset and the augmented datasets separately. A neural network training/testing ecosystem named as supervisely with Tesla K80 GPU was used for YOLO v3 model training on all datasets. Results: The results showed that the DA techniques rotate at 180o and rotate at 90o performed the best as data enhancement techniques for medical imaging. Conclusion: Rotation techniques are found significant to enhance the low volume of medical imaging datasets.

Abstract. The range and availability of mobile applications is expanding rapidly. With the increased processing power available on portable devices, developers are increasing the range of services by embracing smartphones in their extensive and diverse practices. While usability testing and evaluations of mobile applications have not yet touched the accuracy level of other web based applications. The existing usability models do not adequately capture the complexities of interacting with applications on a mobile platform. Therefore, this study aims to presents review on existing usability models for mobile applications. These models are in their infancy but with time and more research they may eventually be adopted. Moreover, different categories of mobile apps (medical, entertainment, education) possess different functional and non-functional requirements thus customized models are required for diverse mobile applications.

In recent years, the advent of low-cost digital and mobile devices has led to a strong expansion of social interventions, including those that try to improve student learning and literacy outcomes. Many of these are focused on improving reading in low-income countries, and particularly among the most disadvantaged. Some of these early efforts have been called successful, but little credible evidence exists for those claims. Drawing on a robust sample of projects in the domain of mobiles for literacy, this article introduces a design solution framework that combines intervention purposes with devices, end users, and local contexts. In combination with a suggested set of purpose-driven methods for monitoring and evaluation, this new framework provides useful parameters for measuring effectiveness in the domain of mobiles for literacy. ABSTRACTIn recent years, the advent of low-cost digital and mobile devices has led to a strong expansion of social interventions, including those that try to improve student learning and literacy outcomes. Literacy and mobiles, 2Many of these are focused on improving reading in low-income countries, and particularly among the most disadvantaged. Some of these early efforts have been called successful, but little credible evidence exists for those claims. Drawing on a robust sample of projects in the domain of mobiles for literacy, this article introduces a design solution framework that combines intervention purposes with devices, end users, and local contexts. In combination with a suggested set of purpose-driven methods for monitoring and evaluation, this new framework provides useful parameters for measuring effectiveness in the domain of mobiles for literacy.Key words: literacy, mobiles, technology, developing countries, success, evidence, teachers, quality of education Literacy and mobiles, 3New technologies are of growing importance around the world, and in many facets of everyday lives and livelihoods. These information and communications technologies (ICTs), especially mobile devices, may have special benefits for learning, both in and out of schools. At the same time, major claims are often made about the success of particular devices, before substantial research has been undertaken. In this article, drawing on Wagner, Murphy, and deKorne (2012) and Wagner (2013), we explore the current state of literacy and mobiles, and recommend ways to incorporate improved monitoring and evaluation (M&E) plans for the future.According to a recent report by UNESCO (2013), this is the first time in history that the world has more connected mobile devices than people. Despite the ubiquity of mobile technologies and their increased use in educational settings, little empirical evidence supports their use for learning. More common are anecdotal accounts that do not necessarily indicate real learning gains or broader contextual impacts.At the same time, available evidence from developing countries reveals that significant progress has been made toward international goals for education....

PM 10 samples were collected during intensive fog days in Islamabad, Pakistan, to investigate the impact of particulate matter on fog formation. The PM 10 concentrations were monitored at the Institute of Space Technology site using a highvolume air sampler and its elemental composition was studied using Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS). Sampling was done for a duration of 24 hours on selected days, including all foggy days in a period from January 2016 to . On most occasions, PM 10 levels were considerably high as compared to permissible limits of both Pak-NEQS and WHO guidelines. It has been observed that the air quality during fog days was much worse, with elevated levels of particulate matter observed during foggy days. The SEM-EDS revealed the presence of different elements including some metals Si, K, Ca, Mg, Zn, Fe, Cr, Pb, Al etc. The morphological studies suggest that most of the particles are crystalline in shape, suggesting their main source as soil. Some samples also showed round spherical shape which refers their anthropogenic source. The sun photometer observations of aerosol optical depth (AOD) and satellite observations from Aqua's Moderate-resolution Imaging Spectro-radiometer (MODIS) showed significant correlation. Moreover, elevated level of AOD were found during heavy fog days. The validated high satellite AOD were associated with high PM 10 concentration during heavy fog days.

Primary malignant melanoma of the intramedullary region of the spinal cord has rarely been reported in the literature. These tumors can have variable appearance on magnetic resonance imaging (MRI) due to different extents of melanin and hemorrhage. Histopathologic confirmation and a comprehensive workup to rule out extra-spinal melanoma are required to make definitive diagnosis. We present a case of a patient diagnosed with primary intramedullary spinal melanoma in his lower thoracic spinal cord who was effectively treated with surgical resection, adjuvant radiation, and adjuvant immunotherapy. Gross total resection (GTR) is most vital in the management of this spinal tumor. Although several studies have established the efficacy of immunotherapy agents in advanced malignant melanoma, the use of these agents has not been studied in primary central nervous system melanomas. This case provides insight into the diagnostic approach and treatment options for this unique malignancy.

Major hurdle faced by many physicians in treating various respiratory tract infections and lung carcinomas is their late or mis-diagnosis. In most respiratory tract infections the manner of infection is not completely understood. Similarly, various lung carcinomas are diagnosed at advance stages at which not only the treatment possibilities are narrowed but the chances of survival are also reduced. So, for the sake of better treatment, the quick and improved diagnostic strategies are suggested. Protein biomarkers fully fit the description in this regard as they have shown great potential in specific diagnosis of many respiratory diseases and also have shown capability in timely pin pointing different stages of lung carcinomas. Many serum biomarkers are presently being used for diagnosis but the efficiency for diagnosis of these biomarkers is lower when used alone. So, physicians are suggested to use the combination of different biomarkers. Moreover, genetic biomarkers are also currently studied to indicate the exact stage of disease, the possible damage occurred, the severity of disease and also for the analysis of the possible body response to the therapeutics.