The digitisation of paper-based assessment using e-standards

The use of computers to automate the process of learning and assessment is used in most educational and commercial institutions today. This is done by creating and storing online or digital learning materials and using them in required tasks. In order to use this principal in the automation of paper-based assessment, a standardised encoding scheme has to be employed. We have attempted an explorative study using the Instructional Management System’s (IMS) Question and Test Interoperability (QTI) specification and IMS Learning Resource Metadata Model (LRMM) to create learning objects (LOs). These LOs were based on traditional paper-based assessments and stored additional information that allowed for their effective sharing. The sharing was achieved by creating an OAI-compliant repository and the unqualified Dublin Core metadata (DC) set to describe them. A group of teachers evaluated the quality of paper-based assessments generated from these LOs and were very optimistic about the automation of this process

A Dynamic Analysis of the Relationship among Human Development, Exports and Economic Growth in Pakistan.

This study investigates the econometrically empirical evidence of both the short-run and long-run interrelationships among human development, exports and economic growth in an ARDL framework for Pakistan. This study also examines causal linkages among the said variables by applying the Augmented Granger Causality test of Toda-Yamamoto (1995). By using data on Pakistan’s real GDP, real exports and Human Development Index (HDI) for the period 1970-71 to 2008-09, three models have been estimated. The results show cointegration between economic growth, physical capital, real exports and human development when human development is taken as dependent variables. Furthermore, unidirectional Granger causality running from real GDP to real exports has been found in Bivariate, Trivariate and Tetravariate causality framework. The inclusion of HDI as a measure of human development reduces the physical capital share in real GDP whereas it improves the robustness of the regression model. Real GDP seems to provide resources to improve human development in only the long-run while human capital accumulation does not seem to accelerate real GDP both in the short-run and the long-run. The empirical results of the study do not support ‘export-led growth hypothesis’ and human capital-based endogenous growth theory in case of Pakistan, however, it does support ‘growth-driven exports hypothesis’ in case of Pakistan. JEL classification: O11 Keywords: Human Development, Exports, Economic Growth, ARDL, Causalit

Epidemiology, distribution and identification of ticks on livestock in Pakistan

Background: Ticks are ectoparasites that transmit a variety of pathogens that cause many diseases in livestock which can result in skin damage, weight loss, anemia, reduced production of meat and milk, and mortality. Aim: The aim of this study was to identify tick species and the distribution on livestock hosts (sheep, goat, dairy cattle, and buffalo) of Punjab, Khyber Pakhtunkhwa Province and Islamabad from October 2019 to November 2020. Materials and Methods: Surveillance was performed to calculate the prevalence of ticks on livestock. Tick prevalence data (area, host, breed, gender, age, and seasonal infestation rate) was recorded and analyzed. Results: A total of 2080 animals were examined from selected farms, and, of these, 1129 animals were tick-infested. A total of 1010 male tick samples were identified to species using published keys. Haemaphysalis punctata, Haemaphysalis sulcata, Hyalomma anatolicum, Hyalomma detritum, Hyalomma dromedarii, Hyalomma excavatum, Hyalomma marginatum, Hyalomma rufipes, Rhipicephalus decoloratus Rhipicephalus microplus, and Rhipicephalus sanguineus were collected from goats, sheep, buffalo, and cattle. The overall rates of tick infestation on livestock were 34.83% (buffalo), 57.11% (cattle), 51.97% (sheep) and 46.94% (goats). Within each species, different breeds demonstrated different proportions of infestation. For cattle breeds, infestation proportions were as follows: Dhanni (98.73%), Jersey (70.84%) and the Australian breed of cattle (81.81%). The Neeli Ravi breed (40%) of buffalo and the Beetal breed (57.35%) of goats were the most highly infested for these species. Seasonally, the highest prevalence of infestation (76.78%) was observed in summer followed by 70.76% in spring, 45.29% in autumn, and 20% in winter. The prevalence of tick infestation in animals also varied by animal age. In goats, animals aged 4−6 years showed the highest prevalence (90%), but in cattle, the prevalence of ticks was highest (68.75%) in 6 months−1-year-old animals. 1−3 years old buffalo (41.07%) and 6 months−1 year sheep (65.78%) had the highest prevalence rate. Females had significantly higher infestation rates (61.12%, 55.56% and 49.26%, respectively) in cattle, sheep, and goats. In buffalo, males showed a higher prevalence (38.46%) rate. Conclusions: This study showed tick diversity, infestation rate, and numerous factors (season, age, and gender of host) influencing tick infestation rate in different breeds of cattle, sheep, goats, and buffalo in Punjab Province, Khyber Pakhtunkhwa Province, and Islamabad, Pakistan. Higher tick burdens and rates of tick-borne disease reduce production and productivity in animals. Understanding tick species’ prevalence and distribution will help to develop informed control measures

The Determinants of India’s Imports: A Gravity Model Approach

In order to understand the India’s import trade with its partners, this paper applies the generalized gravity model to analyse the import structure by employing the panel data estimation technique. The results portray that India’s imports are determined by the inflation rates, per capita income differentials and the overall openness of the countries involved in trade. It has been also found out that imports are influenced to a great degree by the common border, as the case is between India, China and Bangladesh. Furthermore, the country precise effects describe that the sway of neighbouring countries is more than that of distant countries on India’s imports

The deleted in brachydactyly B domain of ROR2 is required for receptor activation by recruitment of Src

The transmembrane receptor 'ROR2' resembles members of the receptor tyrosine kinase family of signalling receptors in sequence but its' signal transduction mechanisms remain enigmatic. This problem has particular importance because mutations in ROR2 are associated with two human skeletal dysmorphology syndromes, recessive Robinow Syndrome (RS) and dominant acting Brachydactyly type B (BDB). Here we show, using a constitutive dimerisation approach, that ROR2 exhibits dimerisation-induced tyrosine kinase activity and the ROR2 C-terminal domain, which is deleted in BDB, is required for recruitment and activation of the non-receptor tyrosine kinase Src. Native ROR2 phosphorylation is induced by the ligand Wnt5a and is blocked by pharmacological inhibition of Src kinase activity. Eight sites of Src-mediated ROR2 phosphorylation have been identified by mass spectrometry. Activation via tyrosine phosphorylation of ROR2 receptor leads to its internalisation into Rab5 positive endosomes. These findings show that BDB mutant receptors are defective in kinase activation as a result of failure to recruit Src

Neuromechanical and environment aware machine learning tool for human locomotion intent recognition

Current research suggests the emergent need to recognize and predict locomotion modes (LMs) and LM transitions to allow a natural and smooth response of lower limb active assistive devices such as prostheses and orthosis for daily life locomotion assistance. This Master dissertation proposes an automatic and user-independent recognition and prediction tool based on machine learning methods. Further, it seeks to determine the gait measures that yielded the best performance in recognizing and predicting several human daily performed LMs and respective LM transitions. The machine learning framework was established using a Gaussian support vector machine (SVM) and discriminative features estimated from three wearable sensors, namely, inertial, force and laser sensors. In addition, a neuro-biomechanical model was used to compute joint angles and muscle activations that were fused with the sensor-based features. Results showed that combining biomechanical features from the Xsens with environment-aware features from the laser sensor resulted in the best recognition and prediction of LM (MCC = 0.99 and MCC = 0.95) and LM transitions (MCC = 0.96 and MCC = 0.98). Moreover, the predicted LM transitions were determined with high prediction time since their detection happened one or more steps before the LM transition occurrence. The developed framework has potential to improve the assistance delivered by locomotion assistive devices to achieve a more natural and smooth motion assistance.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015, and part by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs -Controlo Inteligente de um Sistema Ortótico Ativo e Autónomo- under Grant NORTE-01-0145-FEDER-030386, and by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Mumps epidemic in Portugal despite high vaccine coverage : preliminary report

A measles, mumps, and rubella (MMR) trivalent vaccine was added to Portugal’s National Immunisation Programme (NIP) in 1987. All vaccines are given at health centres, free of charge, but an epidemic of mumps began in 1995, firstly in northern Portugal and has now spread to other areas. Initially, only one dose of MMR vaccine at the age of 15 months was recommended. In 1990 a second dose at the age of 11 to 13 years was added. Portugal was one of the first countries in Europe to advocate this policy

Financial crises and the attainment of the SDGs: an adjusted multidimensional poverty approach

This paper analyses the impact of financial crises on the Sustainable Development Goal of eradicating poverty. To do so, we develop an adjusted Multidimensional Poverty Framework (MPF) that includes 15 indicators that span across key poverty aspects related to income, basic needs, health, education and the environment. We then use an econometric model that allows us to examine the impact of financial crises on these indicators in 150 countries over the period 1980–2015. Our analysis produces new estimates on the impact of financial crises on poverty’s multiple social, economic and environmental aspects and equally important captures dynamic linkages between these aspects. Thus, we offer a better understanding of the potential impact of current debt dynamics on Multidimensional Poverty and demonstrate the need to move beyond the boundaries of SDG1, if we are to meet the target of eradicating poverty. Our results indicate that the current financial distress experienced by many low-income countries may reverse the progress that has been made hitherto in reducing poverty. We find that financial crises are associated with an approximately 10% increase of extreme poor in low-income countries. The impact is even stronger in some other poverty aspects. For instance, crises are associated with an average decrease of government spending in education by 17.72% in low-income countries. The dynamic linkages between most of the Multidimensional Poverty indicators, warn of a negative domino effect on a number of SDGs related to poverty, if there is a financial crisis shock. To pre-empt such a domino effect, the specific SDG target 17.4 on attaining long-term debt sustainability through coordinated policies plays a key role and requires urgent attention by the international community

Erratum to: A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies.

DNA methylation plays a fundamental role in the regulation of the genome, but the optimal strategy for analysis of genome-wide DNA methylation data remains to be determined. We developed a comprehensive analysis pipeline for epigenome-wide association studies (EWAS) using the Illumina Infinium HumanMethylation450 BeadChip, based on 2,687 individuals, with 36 samples measured in duplicate. We propose new approaches to quality control, data normalisation and batch correction through control-probe adjustment and establish a null hypothesis for EWAS using permutation testing. Our analysis pipeline outperforms existing approaches, enabling accurate identification of methylation quantitative trait loci for hypothesis driven follow-up experiments