Search

STEM Fellowship’s High School Big Data Challenge is an inquiry-driven experiential learning program that provides students an opportunity to learn and apply the fundamentals of data science – a crucial skill set for a young researcher in the digital age – through independent research projects. The COVID-19 pandemic disrupted high school education, at the same time creating a “fertile ground” for interdisciplinary, student-driven STEM education. This year, we invited students to explore issues of Affordable and Clean Energy at the Individual and Community Levels and to suggest their own evidence-based solutions, using Open Data and the principles of Open Science. Students explored many topics, ranging from Greenhouse Gas Emissions of School Buses to Legitimacy of Electric Vehicles to be the Greener Alternative We developed in-depth learning modules designed to bridge the gap between traditional high school courseware and digital reality and computational science. The students learnt a broad range of data analytics tools and programming languages which are useful for uncovering hidden patterns, trends in structured and unstructured data. Some of the tools the students learnt and used include Python, R, LaTeX, and machine learning. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, Digital Science, Infor, SCWST, CISCO Networking Academy, Canadian Science Publishing, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. It has been a privilege for us to witness the analytical capabilities of the data-native generation of students first hand, and we are certain all entrants will continue to demonstrate excellence in their respective academic and professional careers.

STEM Fellowship’s High School Big Data Challenge is an inquiry-driven experiential learning program that provides students an opportunity to learn and apply the fundamentals of data science – a crucial skill set for a young researcher in the digital age – through independent research projects. The COVID-19 pandemic disrupted high school education, at the same time creating a “fertile ground” for interdisciplinary, student-driven STEM education. This year, we invited students to explore issues of Equality and Equal Access in Education and to suggest their own evidence-based solutions, using Open Data and the principles of Open Science. Students explored many topics, ranging from using machine learning to find hidden socioeconomic factors in access to education, to the efficacy of various modes of instruction. We developed in-depth learning modules designed to lead the student from zero-knowledge to an elementary working proficiency in data science. The students learn a broad range of data analytics tools and programming languages which are useful for uncovering hidden patterns, trends in structured and unstructured data. Some of the tools the students learnt and used includes Python, R, LaTeX, and machine learning. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, Digital Science, Kimberly Foundation, SCWST, CISCO Academy, Canadian Science Publishing, Faculty of Science UofC. It has been a privilege for us to witness the analytical capabilities of the next generation of students firsthand, and we are certain all entrants will continue to demonstrate excellence in their respective careers.

IntroductionFabry disease (FD) is a rare metabolic disorder caused by the genetic deficiency of the lysosomal hydrolase alpha-galactosidase A, causing common and serious kidney complications. We report the first case of Fabry disease detected in Malaysia. Clinical ScenarioA 35-year-old man had an incidental finding of proteinuria during routine health screening. His symptoms included frothy urine and intermittent lower limb swelling. Renal biopsy showed features of Fabry’s disease which was confirmed with genetic testing. He had classical hemizygous Fabry disease of c.610 C>T with Fabry nephropathy, cardiomyopathy, and cornea verticillata. He was then started on be Enzyme Replacement Therapy (ERT) with agalsidase till date. ConclusionProteinuria is the predominant factor in Fabry nephropathy predicting renal disease progression rate. Early Fabry Disease diagnosis could prevent kidney disease progression through the timely initiation of treatment which will help enhance the quality of life of the patient, as the disease progresses.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women's physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI): [0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC = 0.87, 95% CI: [0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC = 0.90, 95% CI: [0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia.

Sarawak has a population that is geographically and characteristically widely varied. In this study we aimed to determine the demographic characteristics of our patient population who undergo continuous ambulatory peritoneal dialysis (CAPD) and to study the incidence, the microbiology and the outcome of CAPD peritonitis.A retrospective record review of all CAPD patients on follow-up at the Miri Hospital, Sarawak, Malaysia from 2014 until 2017 was done.During the 4-year period, the overall peritonitis rate was 0.184 episodes per patient-year. Gram-positive and gram-negative bacteria each constituted one-third of the peritonitis; fungi (2.6%), Mycobacterium tuberculosis (MTB) (5.3%), polymicrobial (2.6%) and sterile culture (26.3%). The most commonly isolated gram-positive bacteria were coagulase-negative Staphylococcus. Our peritonitis rate is comparable to that of other centres i.e., Japan 0.195 and Indonesia 0.25. In comparison, countries like India (0.41), Korea (0.40) and Singapore (0.59) had relatively higher rate of PD-associated peritonitis. Two tuberculosis peritonitis patients died. The rate of catheter removal was approximately 20%. Gram-negative bacteria and MTB have a higher risk of catheter loss. About one-fifth used rainwater to clean their CAPD exit site. Out of this group, 33% did not boil the rainwater prior to usage.Patient's characteristics and microbial susceptibility vary in different places of practice. The high rates of culture-negative peritonitis and high mortality risks associated with TB peritonitis warrant special attention. In patients with refractory peritonitis, early catheter removal is warranted in order to reduce mortality and minimize damage to peritoneal membrane.

Systemic Arcanobacterium pyogenes is a rare bacterial infection in humans.1The diagnosis of thrombotic thrombocytopenic purpura (TTP)-like syndrome and infective endocarditis (IE) is often elusive. We report a case of TTP-like syndrome associated with A. pyogenes endocarditis in a post-allogenic transplant patient.

Distal arthrogryposis (DA) syndromes are a group of disorders characterized by multiple congenital contractures. DA type 2A (DA2A or Freeman-Sheldon syndrome), caused by mutations in MYH3, is typically considered the most severe of the DA syndromes. However, there is wide phenotypic variability among individuals with DA2A. We characterized genotype-phenotype relationships in 46 families with DA2A. MYH3 mutations were found in 43/46 (93%) kindreds, with three mutations (p.T178I, p.R672C, and p.R672H) explaining 39/43 (91%) of cases. Phenotypic severity varied significantly by genotype (P=0.0055). Individuals with p.T178I were the most severely affected with both facial contractures and congenital scoliosis. Classification of individuals with DA2A into phenotypic groups of varying severity should facilitate providing families with more accurate information about natural history and suggests that individuals might benefit from personalized medical management motivated by MYH3 genotype.

This paper investigates High Pressure and/or High Temperature (HPHT) design of a pipeline across an escarpment with significant seabed undulations and elevation change from drill center (DC) to riser touch down point (TDP). The pipeline has a tendency to walk towards the riser during pipeline start-up / shut-down (SD) operations due to seabed slope and riser bottom tension in a case study. A hold-back pile at the uphill pipeline end near DC to arrest walking, along with the riser bottom tension applied at the other end of the pipeline, results in significant tensions in the pipeline and substantial spans over escarpment. The pipeline spanning lengths, lateral buckling amplitudes and walking distances and their variations during the start-up / shut-down cycles are presented and discussed. Both wet-insulated single pipe (WISP) and pipe-in-pipe (PIP) options are studied and compared. Interactions among pipeline global walking, spanning and lateral buckling are observed. In-depth understanding of pipeline systematic response will help perform a safe and cost-effective pipeline design.Copyright © 2014 by ASME

Abstract The interest in the numerical modeling of a deepwater pipe-in-pipe system (PiP) at a free span location is growing. In a free span, the inner pipe bends substantially due to internal pressure and temperature and possibly contacts the outer pipe. Concerns arise how the interaction between them affects the PiP behavior under thermal expansion and contraction. The study is motivated by the need to understand how a PiP at a free span can be modeled adequately. Different finite element (FE) approaches using Abaqus have been practiced. The pipes were simulated with pipe, elbow, and shell elements. The interaction between the inner and outer pipes was simulated with multi-point constraints (MPC), tube-to-tube contact elements (ITT), and contact surfaces. FE model accuracy and cost effectiveness were compared in a case study. The conclusions in this paper would also be applied for PiP sections with bending, such as for lateral buckling of a PiP laid on the seabed and upheaval buckling of a buried PiP. Introduction A PiP insulates the inner pipe using an outer pipe as shown in Figure 1(a). In a free span, Figure 1(b), the pipes bend and the gap between them allow for relative displacement due to different effective tension and weight of the pipes. A shell FE model of a PiP at a span was developed to investigate the behavior during operating and cool down conditions. In addition, pipe FE models were developed and evaluated against the shell element model. It is shown that while the shell FE model simulates the motion of the inner pipe relative to the outer pipe and the contact between them, it requires a lot of computational effort. Contrary, a pipe FE model provides global deformation and pipe stresses close to those of the shell FE model and is cost effective. Table 1 and Table 2 summarize the PiP properties, span under investigation, and loading conditions for each pipe.