An Automated Cloud-based Big Data Analytics Platform for Customer Insights

Product reviews have a significant influence on strategic decisions for both businesses and customers on what to produce or buy. However, with the availability of large amounts of online information, manual analysis of reviews is costly and time consuming, as well as being subjective and prone to error. In this work, we present an automated scalable cloud-based system to harness big customer reviews on products for gaining customer insights through data pipeline from data acquisition, analysis to visualisation in an efficient way. The experimental evaluation has shown that the proposed system achieves good performance in terms of accuracy and computing time

Gastritis Induced ST Segment Elevation on Electrocardiogram

ST segment elevation on an electrocardiogram (EKG) is an alarming finding that warrants an urgent coronary angiogram. Early diagnosis and intervention is extremely important in the setting of Acute Coronary Syndrome (ACS) to prevent irreversible myocardium damage and reduce the mortality rate. However, it is very important to know that not all ST- Elevations (STE) on EKG are due to myocardial infraction. Etiologies can be divided into cardiac and non-cardiac causes. Cardiac causes can include coronary aneurysm and acute pericarditis while non-cardiac causes can include acute cholecystitis and pulmonary embolism. In this paper, we are presenting a unique case of a patient with inferior STE on EKG that was found to be induced by gastritis. Knowing that this condition exists will help prevent patients from undergoing unnecessary interventions

The Self-Supervised Spectral–Spatial Vision Transformer Network for Accurate Prediction of Wheat Nitrogen Status from UAV Imagery

Nitrogen (N) fertilizer is routinely applied by farmers to increase crop yields. At present, farmers often over-apply N fertilizer in some locations or at certain times because they do not have high-resolution crop N status data. N-use efficiency can be low, with the remaining N lost to the environment, resulting in higher production costs and environmental pollution. Accurate and timely estimation of N status in crops is crucial to improving cropping systems’ economic and environmental sustainability. Destructive approaches based on plant tissue analysis are time consuming and impractical over large fields. Recent advances in remote sensing and deep learning have shown promise in addressing the aforementioned challenges in a non-destructive way. In this work, we propose a novel deep learning framework: a self-supervised spectral–spatial attention-based vision transformer (SSVT). The proposed SSVT introduces a Spectral Attention Block (SAB) and a Spatial Interaction Block (SIB), which allows for simultaneous learning of both spatial and spectral features from UAV digital aerial imagery, for accurate N status prediction in wheat fields. Moreover, the proposed framework introduces local-to-global self-supervised learning to help train the model from unlabelled data. The proposed SSVT has been compared with five state-of-the-art models including: ResNet, RegNet, EfficientNet, EfficientNetV2, and the original vision transformer on both testing and independent datasets. The proposed approach achieved high accuracy (0.96) with good generalizability and reproducibility for wheat N status estimation

Metachromatic Leukodystrophy: A Case of Triplets With the Late Infantile Variant and a Systematic Review of the Literature

Metachromatic Leukodystrophy is a rare disorder with great clinical variability. We report the first case of triplets with the late infantile form of the disease and their systematic progression of symptoms. We reviewed the literature and identified all human studies that reported new cases since 1921. We analyzed survival by decade to assess the impact of historical changes in management of care. Mean age at death and 5-year survival from onset of symptoms for late infantile, juvenile and adult phenotype were 4.2 years and 24.9%, 17.4 years and 70.3%, and 43.1 years and 88.6% respectively. 5-year survival of cases reported after 1990 was significantly better than cases reported before 1970 in all subtypes of metachromatic leukodystrophy (late infantile: 52% vs. 14%, juvenile: 100% vs. 46%, adult: 95% vs. 67%). Survival in the late infantile subtype was worse than in other subtypes. Survival significantly improved over time in all subtypes

Effects of non-uniform root zone salinity on water use, Na+ recirculation, and Na+ and H+ flux in cotton

A new split-root system was established through grafting to study cotton response to non-uniform salinity. Each root half was treated with either uniform (100/100 mM) or non-uniform NaCl concentrations (0/200 and 50/150 mM). In contrast to uniform control, non-uniform salinity treatment improved plant growth and water use, with more water absorbed from the non- and low salinity side. Non-uniform treatments decreased Na+ concentrations in leaves. The [Na+] in the ‘0’ side roots of the 0/200 treatment was significantly higher than that in either side of the 0/0 control, but greatly decreased when the ‘0’ side phloem was girdled, suggesting that the increased [Na+] in the ‘0’ side roots was possibly due to transportation of foliar Na+ to roots through phloem. Plants under non-uniform salinity extruded more Na+ from the root than those under uniform salinity. Root Na+ efflux in the low salinity side was greatly enhanced by the higher salinity side. NaCl-induced Na+ efflux and H+ influx were inhibited by amiloride and sodium orthovanadate, suggesting that root Na+ extrusion was probably due to active Na+/H+ antiport across the plasma membrane. Improved plant growth under non-uniform salinity was thus attributed to increased water use, reduced leaf Na+ concentration, transport of excessive foliar Na+ to the low salinity side, and enhanced Na+ efflux from the low salinity root