A Framework for Data-Driven Solutions with COVID-19 Illustrations

Data–driven solutions have long been keenly sought after as tools for driving the world’s fast changing business environment, with business leaders seeking to enhance decision making processes within their organisations. In the current era of Big Data, applications of data tools in addressing global, regional and national challenges have steadily grown in almost all fields across the globe. However, working in silos has continued to impede research progress, creating knowledge gaps and challenges across geographical borders, legislations, sectors and fields. There are many examples of the challenges the world faces in tackling global issues, including the complex interactions of the 17 Sustainable Development Goals (SDG) and the spatio–temporal variations of the impact of the on-going COVID–19 pandemic. Both challenges can be seen as non–orthogonal, strongly correlated and requiring an interdisciplinary approach to address. We present a generic framework for filling such gaps, based on two data-driven algorithms that combine data, machine learning and interdisciplinarity to bridge societal knowledge gaps. The novelty of the algorithms derives from their robust built–in mechanics for handling data randomness. Animation applications on structured COVID–19 related data obtained from the European Centre for Disease Prevention and Control (ECDC) and the UK Office of National Statistics exhibit great potentials for decision-support systems. Predictive findings are based on unstructured data–a large COVID–19 X–Ray data, 3181 image files, obtained from GitHub and Kaggle. Our results exhibit consistent performance across samples, resonating with cross-disciplinary discussions on novel paths for data-driven interdisciplinary research

Dealing with Randomness and Concept Drift in Large Datasets

Data-driven solutions to societal challenges continue to bring new dimensions to our daily lives. For example, while good-quality education is a well-acknowledged foundation of sustainable development, innovation and creativity, variations in student attainment and general performance remain commonplace. Developing data -driven solutions hinges on two fronts-technical and application. The former relates to the modelling perspective, where two of the major challenges are the impact of data randomness and general variations in definitions, typically referred to as concept drift in machine learning. The latter relates to devising data-driven solutions to address real-life challenges such as identifying potential triggers of pedagogical performance, which aligns with the Sustainable Development Goal (SDG) #4-Quality Education. A total of 3145 pedagogical data points were obtained from the central data collection platform for the United Arab Emirates (UAE) Ministry of Education (MoE). Using simple data visualisation and machine learning techniques via a generic algorithm for sampling, measuring and assessing, the paper highlights research pathways for educationists and data scientists to attain unified goals in an interdisciplinary context. Its novelty derives from embedded capacity to address data randomness and concept drift by minimising modelling variations and yielding consistent results across samples. Results show that intricate relationships among data attributes describe the invariant conditions that practitioners in the two overlapping fields of data science and education must identify

A robust domain partitioning intrusion detection method

The capacity for data mining algorithms to learn rules from data is influenced by, inter-alia, the random nature of training and test data as well as by the diversity of domain partitioning models. Isolating normal from malicious data traffic across networks is one regular task that is naturally affected by that randomness and diversity. We propose a robust algorithm Sample-Measure-Assess (SMA) that detects intrusion based on rules learnt from multiple samples. We adapt data obtained from a set of simulations, capturing data attributes identifiable by number of bytes, destination and source of packets, protocol and nature of data flows (normal and abnormal) as well IP addresses. A fixed sample of 82,332 observations on 27 variables was drawn from a superset of 2.54 million observations on 49 variables and multiple samples were then repeatedly extracted from the former and used to train and test multiple versions of classifiers, via the algorithm. With two class labels–binary and multi-class, the dataset presents a classic example of masked and spurious groupings, making an ideal case for concept learning. The algorithm learns a model for the underlying distributions of the samples and it provides mechanics for model assessment. The settings account for our method’s novelty–i.e., ability to learn concept rules from highly masked to highly spurious cases while observing model robustness. A comparative analysis of Random Forests and individually grown trees show that we can circumvent the former’s dependence on multicollinearity of the trees and their individual strength in the forest by proceeding from dimensional reduction to classification using individual trees. Given data of similar structure, the algorithm can order the models in terms of optimality which, means our work can contribute towards understanding the concept of normal and malicious flows across tools. The algorithm yields results that are less sensitive to violated distributional assumptions and, hence, it yields robust parameters and provides a generalisation that can be monitored and adapted to specific low levels of variability. We discuss its potential for deployment with other classifiers and potential for extension into other applications, simply by adapting the objectives to specific conditions

Effect of nocturnal hypoxemia on glycemic control among diabetic Saudi patients presenting with obstructive sleep apnea

BackgroundObstructive sleep apnea (OSA) is a prevalent disease that is associated with an increased incidence of type II diabetes mellitus (DM) if left untreated. We aimed to determine the association between glycosylated hemoglobin (HbA1c) levels and both nocturnal hypoxemia and apnea-hypopnea index (AHI) among a Saudi patients with OSA.MethodsA cross-sectional study that enrolled 103 adult patients diagnosed with DM and confirmed to have OSA by full night attended polysomnography between 2018 and 2021. Those who presented with acute illness, chronic obstructive pulmonary disease (COPD)/restrictive lung diseases causing sleep-related hypoxemia, or no available HbA1c level within 6 months before polysomnography were excluded from the study. Univariate and multivariate linear regression analyses between HbA1c levels and parameters of interest were tested.ResultsSixty-seven (65%) of the studied population had uncontrolled DM (HbA1c ≥7%). In univariate regression analysis, there was a significant positive association between HbA1c, and sleep time spent with an oxygen saturation below 90% (T90), female gender, and body mass index (BMI) (p<0.05) but not AHI, or associated comorbidities (p>0.05). In the multivariate analysis, HbA1c was positively associated with increasing T90 (p<0.05), and ODI (p<0.05), but not with AHI (p>0.05).ConclusionNocturnal hypoxemia could be an important factor affecting glycemic control in patients with OSA suffering from DM irrespective of the severity of both diseases

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

A data-based method for harmonising heterogeneous data modelling techniques across data mining applications

We propose an iterative graphical data visualisation algorithm for optimal model selection. The algorithm is implemented on three domain-partitioning techniques - decision trees, neural networks and support vector machines. Each model is trained and tested on the Pima Indians and Bupa Liver Disorders datasets with the performance being assessed in a multi-step process. Firstly, the conventional ROC curves and the Youden Index are applied to determine the optimal model then sequential moving differences involving the fitted parameters - true and false positives – are extracted and their respective probability density estimations are used to track their variability using the proposed algorithm. The algorithm allows the use of data-dependent density bandwidths as tuning parameters in determining class separation across applications. Our results suggest that this novel approach yields robust predictions and minimizes data obscurity and over-fitting. The algorithm’s simple mechanics which derive from the standard confusion matrix and built-in graphical data visualisation and adaptive bandwidth features make it multidisciplinary compliant and easily comprehensible to non-specialists. The paper’s main outcomes are two-fold. Firstly, it combines the power of domain partitioning techniques on Bayesian foundations with graphical data visualisation to provide a dynamic, discernible and comprehensible information representation. Secondly, it demonstrates that by converting mathematical formulation into visual objects, multi-disciplinary teams can jointly enhance the knowledge of concepts and positively contribute towards global consistency in the data-based characterisation of various phenomena across disciplines

Sustanon induces dose-independent hypertrophy and satellite cell proliferation in slow oxidative fibers of avian skeletal muscle

Sustanon is a well-known anabolic drug that is used to treat hypogonadism and restore muscle mass and bone density. As research to date has been limited to its effects in glycolytic fibers, this study aimed to investigate the dose-related effects of Sustanon on the oxidative fibers of avian skeletal muscle. Adult female chickens were randomly divided into 4 groups: control (C), received a dose of 100 μl normal saline per injection; and Sustanon-1, -2, and -3 (S1, S2, and S3), that received a dose of 12.5, 25, or 50 mg/kg Sustanon per injection, respectively. Each bird received 4 injections at weekly intervals (1 injection/week). Robust histochemical and immunofluorescent techniques along with morphometric analyses were applied to determine the oxidative activity and morphological variations of the oxidative muscle fibers in all groups. Sustanontreated groups exhibited significant increases in fiber size and numbers of satellite cells and myonuclei compared to the control group. However, no significant variations were found between Sustanon-treated groups in the aforementioned indices. In conclusion, Sustanon induced oxidative fiber hypertrophy that was associated with satellite cell proliferation and myonuclear accretion in avian skeletal muscle. Furthermore, the effects of Sustanon appeared to be dose-independent

Differential response of oxidative and glycolytic skeletal muscle fibers to mesterolone

International audienceOxidative and glycolytic muscle fibers differ in their ultrastructure, metabolism, and responses to physiological stimuli and pathological insults. We examined whether these fibers respond differentially to exogenous anabolic androgenic steroids (AASs) by comparing morphological and histological changes between the oxidative anterior latissimus dorsi (ALD) and glycolytic pectoralis major (PM) fibers in adult avian muscles. Adult female White Leghorn chickens (Gallus gallus) were randomly divided into five groups: a vehicle control and four mesterolone treatment groups (4, 8, 12, and 16 mg/kg). Mesterolone was administered orally every three days for four weeks. Immunocytochemical techniques and morphometric analyses were employed to measure the changes in muscle weight, fiber size, satellite cell (SC) composition, and number of myonuclei. Mesterolone increased both body and muscle weights and induced hypertrophy in glycolytic PM fibers but not in oxidative ALD fibers. Mesterolone induced SC proliferation in both muscles; however, the myonuclear accretion was noticeable only in the PM muscle. In both muscles, the collective changes maintained a constant myonuclear domain size and the changes were dose independent. In conclusion, mesterolone induced distinct dose-independent effects in avian oxidative and glycolytic skeletal muscle fibers; these findings might be clinically valuable in the treatment of age-related sarcopenia

Daytime versus nighttime laparoscopic appendectomy in term of complications and clinical outcomes: A retrospective study of 1001 appendectomies

Purpose: This research aims to study whether the time of day impacts the outcome of laparoscopic appendectomy. Peri/post-operative data including type of surgery, operating room time, length of stay, re-hospitalization rates, and short/long term morbidity and mortality were assembled and analyzed. Methods: A retrospective review of all patient's charts who underwent an appendectomy for acute appendicitis at the Acute Care Surgery division at Hamad Medical Corporation (HMC) from December 2017 to July 2020 was performed. Our institution implemented SAGES protocol to patients with laparoscopic appendectomy. Medical history, symptoms, duration of symptoms, type of treatment, complication, experience level of surgeons in different shift, morbidity, mortality, and length of hospital stay were extracted and analyzed. Results: Multivariate logistic regression analysis was used to identify the odds ratio (OR) and the correlation of variables with different surgical groups. A total of 1001 patients were included in this study and underwent laparoscopic appendectomy, 51.3% were operated during the daytime shifts and 48.7% during the nighttime shifts. The majority of surgeries were operated during the nighttime shift C (1:00 a.m. to 7:00 a.m.). Neither there was any difference in clinical outcomes nor day/night operation time with physicians. A statistically significant correlation was found between hospital-stay of patients with different surgical group (OR: 2.13, 95% CI: 0.75–0.93, P < 0.001). Conclusion: Appendectomy conducted at night is correlated with similar complications as appendectomy performed during the day, and that the varied shift hours had no effect on the complication rates or on the quality of care provided to patients at our hospital