An Empirical Analysis of the Linder

This paper presents empirical evidence in support of the Linder theory of international trade for three of the South Asian countries, Bangladesh, India, and Pakistan. This finding implies that these countries trade more intensively with countries of other regions, which may have similar per capita income levels, as predicted by Linder in his hypothesis. The contribution of this research is threefold: first, there is new information on the Linder hypothesis by focusing on South Asian countries; second, this is one of very few analyses to capture both time-series and cross-section elements of the trade relationship by employing a panel data set; third, the empirical methodology used in this analysis corrects a major shortcoming in the existing literature by using a censored dependent variable in estimation.

An Empirical Analysis of the Linder Theory of International Trade for South Asian Countries.

This paper presents empirical evidence in support of the Linder theory of international trade for three of the South Asian countries, Bangladesh, India, and Pakistan. This finding implies that these countries trade more intensively with countries of other regions, which may have similar per capita income levels, as predicted by Linder in his hypothesis. The contribution of this research is threefold: first, there is new information on the Linder hypothesis by focusing on South Asian countries; second, this is one of very few analyses to capture both time-series and cross-section elements of the trade relationship by employing a panel data set; third, the empirical methodology used in this analysis corrects a major shortcoming in the existing literature by using a censored dependent variable in estimation

Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020

Anthropogenic activities and natural climate changes are the central driving forces of global ecosystems and agriculture changes. Climate changes, such as rainfall and temperature changes, have had the greatest impact on different types of plant production around the world. In the present study, we investigated the spatiotemporal variation of major crops (cotton, rice, wheat, and sugarcane) in the District Vehari, Pakistan, from 1984 to 2020 using remote sensing (RS) technology. The crop identification was pre-processed in ArcGIS software based on Landsat images. After pre-processing, supervised classification was used, which explains the maximum likelihood classification (MLC) to identify the vegetation changes. Our results showed that in the study area cultivated areas under wheat and cotton decreased by almost 5.4% and 9.1% from 1984 to 2020, respectively. Vegetated areas have maximum values of NDVI (>0.4), and built-up areas showed fewer NDVI values (0 to 0.2) in the District Vehari. During the Rabi season, the temperature was increased from 19.93 °C to 21.17 °C. The average temperature was calculated at 34.28 °C to 35.54 °C during the Kharif season in the District Vehari. Our results showed that temperature negatively affects sugarcane, rice, and cotton crops during the Rabi season, and precipitation positively affects sugarcane, rice, and cotton crops during the Kharif season in the study area. Accurate and timely assessment of crop estimation and relation to climate change can give very useful information for decision-makers, governments, and planners in formulating policies regarding crop management and improving agriculture yields

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

Assessing Yield Response and Relationship of Soil Boron Fractions with Its Accumulation in Sorghum and Cowpea under Boron Fertilization in Different Soil Series

Boron (B) is an essential micronutrient in the growth of reproductive plant parts. Its deficiency and/or toxicity are widespread in arid and semi-arid soils with low clay contents. This study was planned to determine the response of sorghum (Sorghum bicolor L., non-leguminous crop) and cowpea (Vigna sinensis L., leguminous crop) to boron (0, 2, 4, and 16 µg g−1) on four distinct soil series from Punjab, Pakistan i.e., Udic Haplustalf (Pindorian region), Typic Torrifluvent (Shahdra region), Halic Camborthid (Khurianwala region), and Udic Haplustalf (Gujranwala region). Overall, there was a significant difference (p < 0.05) in yield between the sorghum (3.8 to 5.5 g pot−1 of 5 kg dry soil) and cowpea (0.2 to 3.2 g pot−1 of 5 kg dry soil) in response to B application. The highest yield was observed in both sorghum and cowpea either in control or at 2 µg g−1 B application in all four soils. Cowpea showed the same yield trend in all four soils (i.e., an increase in yield at 2 µg g−1 B application, followed by a significant decrease at the higher B levels). In contrast, sorghum exhibited greater variability of response on different soils; Udic Haplustalf (Pindorian region) produced the greatest yield at low levels of B application. However, Halic Camborthid produced its lowest yield at that level. Boron concentration in shoots increased with the levels of B application, particularly in sorghum. In cowpea, the plant growth was extremely retarded—and most of the plants died at higher levels of B application even if a lower concentration of B was measured within the shoot. Hot water-extractable B was the most available fraction for cowpea (R2 = 0.96), whereas the easily exchangeable B was most available for sorghum (R2 = 0.90). Overall, these results have implications for micronutrient uptake for both leguminous and non-leguminous crops

Deep Learning based Classification of Thyroid Cancer using Different Medical Imaging Modalities : A Systematic Review

Deep learning algorithms have achieved a tremendous triumph in task-specific feature classification. Deep learning methods are very much effective when a large amount of training data is scarce. It has been significantly applied for disease classification from medical imaging. The paper aims to identify and summarize the scenario of current research on thyroid cancer using deep learning methods through different medical imaging modalities which are found at present so that reseachers become capable to select a useful and the most relevant approach which might be fruitful in dealing with thyroid cancer. This may also raise a need for more work out while dealing with future challenges. This Systematic literature review (SLR) has been presented by reviewing research articles published in well-reputed venues between 2017 to 2021. A comprehensive review was performed to appraise the deep learning approaches that have been applied in classifying a thyroid nodule disorder from different medical imaging modalities. The analysis is performed based on different parameters reported in selected research studies which include classification accuracy, true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN) sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). A total of 2,149 research studies have been obtained by applying search queries in different journals’ databases, out of them 40 papers have been selected for this SLR. Among them 22 studies have contributed sufficiently to the construction of the evaluation table which enabled the test process of methods of deep learning, having sensitivity varies between 75% to 100% (mean 89.50%) and specificity ranged from 64% to 100% (mean 84.4 %). The outputs revealed that the Convolutional Neural Network (CNN) has produced significant accuracy and has been extensively applied in the diagnosis of thyroid cancer by medical professionals. Furthermore, it is concluded that if the thyroid cancer exposure is inappropriate then it may restrict the deep learning mechanism and make its reliability challenge able

Enhancing Drought Tolerance in Wheat Cultivars through Nano-ZnO Priming by Improving Leaf Pigments and Antioxidant Activity

Climate change, global warming, stagnant productivity of wheat and food security concerns owing to frequent spells of drought stress (DS) have necessitated finding biologically viable drought-mitigation strategies. A trial was conducted to test two promising wheat cultivars (Ujala-16 and Zincol-16) that were subjected to pre-sowing priming treatments with different doses of ZnO nanoparticles (NPs = 40, 80, 120 and 160 ppm) under 50% and 100% field capacity (FC) conditions. The ZnO NPs were prepared with a co-precipitation method and characterized through X-ray diffraction (XRD) and with a scanning electron microscope (SEM). For comparison purposes, untreated seeds were sown as the control treatment. The response variables included botanical traits (lengths, fresh and dry wrights of root and shoot), chlorophyll (a, b and total) contents, antioxidant and proline contents and nutrients status of wheat cultivars. The results showed that DS significantly decreased all traits of wheat cultivars, while ZnO NPs, especially the 120 ppm dose, remained superior by increasing all botanical traits at 100% FC. In addition, ZnO NPs increased the chlorophyll a (1.73 mg/g FW in Ujala-16 and 1.75 mg/g FW in Zincole-16) b (0.70 mg/g FW in Ujala-16 and 0.71 mg/g FW in Zincole-16) and total chlorophyll content (2.43 mg/g FW in Ujala-16 and 2.46 mg/g FW in Zincole-16) by improving the activity of antioxidant and proline content. Moreover, plant nutrients such as Ca, Mg, Fe, N, P, K, and Zn contents were increased by ZnO NPs, especially in the Zincol-16 cultivar. To summarize, Zincol-16 remains superior to Ujala-16, while ZnO NPs (120 ppm dose under 100% FC) increases the growth and mineral contents of both wheat varieties. Thus, this combination might be recommended to wheat growers after testing further in-depth evaluation of more doses of ZnO NPs

Abstract P003: Comparison of Ideal Cardiovascular Health Status Across National Representative Cohorts: A Systematic Review

Introduction: In 2010, the American Heart Association set 2020 national goals for promoting cardiovascular health (CVH), emphasizing measurement of reproducible health behaviors and health factors ..