A very rare vascular complication of the Edwards expandable eSheath during transcatheter aortic valve replacement.

Transcatheter aortic valve replacement (TAVR) has revolutionized the management of severe symptomatic aortic valve stenosis. TAVR is now indicated as an alternative to surgical replacement in a wide risk profile ranging from high to low surgical risk. Although vascular complications have decreased in frequency over time, with the introduction of lower profile delivery systems and sheaths, they remain one of the most frequently encountered and serious complications of TAVR. Patient-specific predisposing factors have been well characterized. However, much less is known about device-specific complications. Awareness of the possible device-related complications may lead to earlier identification, prompt management, and better outcomes. We report a previously unreported complication of the Edwards expandable eSheath that lead to avulsion of the external iliac artery following successful TAVR with a 29-mm Edwards Sapien 3 transcatheter heart valve. Bleeding was promptly controlled with an occlusion balloon and emergency surgical repair was required with a favorable outcome.

Routine postaccess-closure angiography to detect vascular complications following transfemoral TAVR.

BACKGROUND: Vascular complications following transfemoral TAVR are associated with increased morbidity and mortality. Measures that may mitigate this risk are important. AIM: To evaluate the utility of routine, access-vessel angiography post sheath-removal in the detection and management of complications in patients undergoing transcatheter aortic valve replacement (TAVR). METHODS: This was a retrospective study of 512 consecutive patients who underwent transfemoral TAVR with routine post access-closure angiography from the radial artery. Rates of mild angiographically evident bleeding, bleeding requiring surgery/interventional-radiology, ischemia, 90-day access-site-related events, and major and minor vascular complications using Valve Academic Research Consortium 3 definitions were recorded. RESULTS: Of 512 patients, digital subtraction angiography (DSA) was undertaken via the radial artery in 467 patients (91%). In the remaining patients (9%) DSA was either not attempted, due to concerns regarding kidney disease and contrast volume, or failed due to anatomical factors (aortic tortuosity/calcification). Significant chronic kidney disease was present at baseline in 72.4% of this cohort (stages III-IV or dialysis). Ninety-four percent of cases underwent TAVR using a balloon-expandable platform. Mild iliofemoral extravasation was observed in 7.7% of the DSA cases. These cases were managed by manual compression with none requiring any vascular intervention subsequently. Valve Academic Research Consortium 3 major and minor access-site-related complications were observed in 0.4% and 12.2%, respectively. Access-site-related bleeding and ischemic events requiring interventional-radiology or vascular-surgery were observed in 0.9% and 1.7% of the DSA cases, respectively. No new renal replacement therapy was needed in any of the DSA cases. Discharge to 90-day access-related complications was 0.8%. CONCLUSIONS: Routine post access-closure angiography is feasible via the radial artery in patients undergoing transfemoral TAVR and appears safe. It facilitates early identification of complications and mitigates risk by enabling prompt action to be taken. Larger studies are needed to confirm these findings.

The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies.

Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0.

An Intelligent Anomaly Detection Approach for Accurate and Reliable Weather Forecasting at IoT Edges: A Case Study.

Industrialization and rapid urbanization in almost every country adversely affect many of our environmental values, such as our core ecosystem, regional climate differences and global diversity. The difficulties we encounter as a result of the rapid change we experience cause us to encounter many problems in our daily lives. The background of these problems is rapid digitalization and the lack of sufficient infrastructure to process and analyze very large volumes of data. Inaccurate, incomplete or irrelevant data produced in the IoT detection layer causes weather forecast reports to drift away from the concepts of accuracy and reliability, and as a result, activities based on weather forecasting are disrupted. A sophisticated and difficult talent, weather forecasting needs the observation and processing of enormous volumes of data. In addition, rapid urbanization, abrupt climate changes and mass digitization make it more difficult for the forecasts to be accurate and reliable. Increasing data density and rapid urbanization and digitalization make it difficult for the forecasts to be accurate and reliable. This situation prevents people from taking precautions against bad weather conditions in cities and rural areas and turns into a vital problem. In this study, an intelligent anomaly detection approach is presented to minimize the weather forecasting problems that arise as a result of rapid urbanization and mass digitalization. The proposed solutions cover data processing at the edge of the IoT and include filtering out the missing, unnecessary or anomaly data that prevent the predictions from being more accurate and reliable from the data obtained through the sensors. Anomaly detection metrics of five different machine learning (ML) algorithms, including support vector classifier (SVC), Adaboost, logistic regression (LR), naive Bayes (NB) and random forest (RF), were also compared in the study. These algorithms were used to create a data stream using the time, temperature, pressure, humidity and other sensor-generated information.

Healthy, nudged, and wise: Experimental evidence on the role of information salience in reducing tobacco intake.

We evaluate the performance of two behavioral interventions aimed at reducing tobacco consumption in an ultra-poor rural region of Bangladesh, where conventional methods like taxes and warning labels are infeasible. The first intervention asked participants to daily log their tobacco consumption expenditure. The second intervention placed two graphic posters with warnings about the harmful effects of tobacco consumption on tobacco users and their children in the sleeping quarters of the participating households. While both interventions reduced household tobacco consumption expenditure, male participants who logged their expenditure substituted cigarettes with cheaper smokeless tobacco. The reduction in tobacco intake is larger among males with a non-tobacco consuming spouse. Exploratory analysis reveals that risk-averse males who spent relatively more on tobacco responded more to the logbook intervention. More educated, patient males with children below age five responded better to the poster intervention. The findings suggest that in countries with multi-tiered tobacco excise tax structures, which incentivize downward substitution, extending complementary demand-side policies that worked elsewhere to the rural poor might be unwise. Instead, policies may leverage something as universal as parental concern for their children's health to promote better health decision-making.

Genomic analyses identify molecular subtypes of pancreatic cancer.

Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-ß, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.

Evaluation of a deep learning method for the automated detection of supraspinatus tears on MRI.

OBJECTIVE: To evaluate if deep learning is a feasible approach for automated detection of supraspinatus tears on MRI. MATERIALS AND METHODS: A total of 200 shoulder MRI studies performed between 2015 and 2019 were retrospectively obtained from our institutional database using a balanced random sampling of studies containing a full-thickness tear, partial-thickness tear, or intact supraspinatus tendon. A 3-stage pipeline was developed comprised of a slice selection network based on a pre-trained residual neural network (ResNet); a segmentation network based on an encoder-decoder network (U-Net); and a custom multi-input convolutional neural network (CNN) classifier. Binary reference labels were created following review of radiologist reports and images by a radiology fellow and consensus validation by two musculoskeletal radiologists. Twenty percent of the data was reserved as a holdout test set with the remaining 80% used for training and optimization under a fivefold cross-validation strategy. Classification and segmentation accuracy were evaluated using area under the receiver operating characteristic curve (AUROC) and Dice similarity coefficient, respectively. Baseline characteristics in correctly versus incorrectly classified cases were compared using independent sample t-test and chi-squared. RESULTS: Test sensitivity and specificity of the classifier at the optimal Youden's index were 85.0% (95% CI: 62.1-96.8%) and 85.0% (95% CI: 62.1-96.8%), respectively. AUROC was 0.943 (95% CI: 0.820-0.991). Dice segmentation accuracy was 0.814 (95% CI: 0.805-0.826). There was no significant difference in AUROC between 1.5 T and 3.0 T studies. Sub-analysis showed superior sensitivity on full-thickness (100%) versus partial-thickness (72.5%) subgroups. DATA CONCLUSION: Deep learning is a feasible approach to detect supraspinatus tears on MRI.

A Survey on 5G and LPWAN-IoT for Improved Smart Cities and Remote Area Applications: From the Aspect of Architecture and Security.

Addressing the recent trend of the massive demand for resources and ubiquitous use for all citizens has led to the conceptualization of technologies such as the Internet of Things (IoT) and smart cities. Ubiquitous IoT connectivity can be achieved to serve both urban and underserved remote areas such as rural communities by deploying 5G mobile networks with Low Power Wide Area Network (LPWAN). The current architectures will not offer flexible connectivity to many IoT applications due to high service demand, data exchange, emerging technologies, and security challenges. Hence, this paper explores various architectures that consider a hybrid 5G-LPWAN-IoT and Smart Cities. This includes security challenges as well as endogenous security and solutions in 5G and LPWAN-IoT. The slicing of virtual networks using software-defined network (SDN)/network function virtualization (NFV) based on the different quality of service (QoS) to satisfy different services and quality of experience (QoE) is presented. Also, a strategy that considers the implementation of 5G jointly with Weightless-N (TVWS) technologies to reduce the cell edge interference is considered. Discussions on the need for ubiquity connectivity leveraging 5G and LPWAN-IoT are presented. In addition, future research directions are presented, including a unified 5G network and LPWAN-IoT architecture that will holistically support integration with emerging technologies and endogenous security for improved/secured smart cities and remote areas IoT applications. Finally, the use of LPWAN jointly with low earth orbit (LEO) satellites for ubiquitous IoT connectivity is advocated in this paper.

Sensor-Based Solid Waste Handling Systems: A Survey.

As a consequence of swiftly growing populations in the urban areas, larger quantities of solid waste also form rapidly. Since urban local bodies are found to be unable to manage this perilous situation effectively, there is a high probability of risks relative to the environment and public health. A sudden change is indispensable in the existing systems that are developed for the collection, transportation, and disposal of solid waste, which are entangled in turmoil. However, Smart sensors and wireless technology enable cyber-physical systems to automate solid waste management, which will revolutionize the industry. This work presents a comprehensive study on the evolution of automation approaches in solid waste management systems. This study is enhanced by dissecting the available literature in solid waste management with Radio Frequency Identification (RFID), Wireless Sensor Networks (WSN), and Internet of Things (IoT)-based approaches and analyzing each category with a typical architecture, respectively. In addition, various communication technologies adopted in the aforementioned categories are critically analyzed to identify the best choice for the deployment of trash bins. From the survey, it is inferred that IoT-based systems are superior to other design approaches, and LoRaWAN is identified as the preferred communication protocol for the automation of solid waste handling systems in urban areas. Furthermore, the critical open research issues on state-of-the-art solid waste handling systems are identified and future directions to address the same topic are suggested.

Additive Manufacturing of Dental Ceramics: A Systematic Review and Meta-Analysis.

PURPOSE: The purpose of this systematic review and meta-analysis was to evaluate the effect of using additive manufacturing (AM) for dental ceramic fabrication in comparison with subtractive manufacturing (SM), and to evaluate the effect of the type of AM technology on dental ceramic fabrication. MATERIALS AND METHODS: A search was conducted electronically in MEDLINE (via PubMed), EBSCOhost, Scopus, and Cochran Library databases, and also by other methods (table of contents screening, backward and forward citations, and grey literature search) up to February 12, 2022, to identify records evaluating additive manufacturing of ceramics for dental purposes in comparison with subtractive manufacturing. A minimum of 2 review authors conducted tstudy selection, quality assessment, and data extraction. Quality assessment was performed with Joanna Briggs Institute tool, and the quantitative synthesis was performed with the Comprehensive Meta-Analysis program (CMA, Biostat Inc). Hedges's g for effect size was calculated, with 0.2 as small, 0.5 as medium, and 0.8 as large. Heterogeneity was assessed with I2 and prediction interval (PI) statistics. Publication bias was investigated with funnel plots and grey literature search. Certainty of evidence was assessed with the Grading of Recommendations: Assessment, Development, and Evaluation (GRADE) tool. RESULTS: A total of 28 studies were included for the qualitative and quantitative synthesis; 11 in vitro studies on accuracy, 1 in vivo study on color, and 16 in vitro studies on physical and mechanical properties. Meta-analysis showed overall higher accuracy for SM compared with AM, with medium effect size (0.679, CI: 0.173 to 1.185, p = 0.009) and also for marginal (g = 1.05, CI: 0.344 to 1.760, p = 0.004), occlusal (g = 2.24, CI: 0.718 to 3.766, p = 0.004), and total (g = 4.544, CI: -0.234 to 9.323, p = 0.062) with large effect size; whereas AM had higher accuracy than SM with small effect size for the external (g = -0.238, CI: -1.215 to 0.739), p = 0.633), and internal (g = -0.403, CI: -1.273 to 0.467, p = 0.364) surfaces. For technology, self-glazed zirconia protocol had the smallest effect size (g = -0.049, CI: -0.878 to 0.78, p = 0.907), followed by stereolithography (g = 0.305, CI: -0.289 to 0.9, p = 0.314), and digital light processing (g = 1.819, CI: 0.662 to 2.976, p = 0.002) technologies. Flexural strength was higher for ceramics made by SM in comparison to AM with large effect size (g = -2.868, CI: -4.371 to -1.365, p < 0.001). Only 1 study reported on color, favoring ceramics made through combined AM and SM. CONCLUSIONS: Subtractive manufacturing had better overall accuracy, particularly for the marginal and occlusal areas, higher flexural strength, and more favorable hardness, fracture toughness, porosity, fatigue, and volumetric shrinkage; whereas AM had more favorable elastic modulus and wettability. Both methods had favorable biocompatibility. All studies on accuracy and mechanical properties were in vitro, with high heterogeneity and low to very low certainty of evidence. There is a lack of studies on color match and esthetics.

tailfindr: alignment-free poly(A) length measurement for Oxford Nanopore RNA and DNA sequencing.

Polyadenylation at the 3'-end is a major regulator of messenger RNA and its length is known to affect nuclear export, stability, and translation, among others. Only recently have strategies emerged that allow for genome-wide poly(A) length assessment. These methods identify genes connected to poly(A) tail measurements indirectly by short-read alignment to genetic 3'-ends. Concurrently, Oxford Nanopore Technologies (ONT) established full-length isoform-specific RNA sequencing containing the entire poly(A) tail. However, assessing poly(A) length through base-calling has so far not been possible due to the inability to resolve long homopolymeric stretches in ONT sequencing. Here we present tailfindr, an R package to estimate poly(A) tail length on ONT long-read sequencing data. tailfindr operates on unaligned, base-called data. It measures poly(A) tail length from both native RNA and DNA sequencing, which makes poly(A) tail studies by full-length cDNA approaches possible for the first time. We assess tailfindr's performance across different poly(A) lengths, demonstrating that tailfindr is a versatile tool providing poly(A) tail estimates across a wide range of sequencing conditions.

Presenting the characteristics, smoking versus diabetes, and outcome among patients hospitalized with COVID-19.

METHODS: We designed a cross-sectional, observational follow-up for 284 COVID-19 patients involving healthy patients, smokers, diabetics, and diabetic plus smokers recruited from May 1, 2020 to June 25, 2020. The clinical features, severity, duration, and outcome of the disease were analyzed. RESULTS: Of 284 COVID-19 patients, the median age was 48 years (range, 18-80), and 33.80% were female. Common symptoms included fever (85.56%), shortness of breath (49.65%), cough (45.42%), and headache (40.86%). Patients with more than one comorbidity (diabetes and smoking) presented as severe-critical cases compared to healthy patients, diabetics, and smokers. Smokers presented with a lower rate of death in comparison to diabetic patients and diabetic + smoking, furthermore, smoking was less risky than diabetes. Although the mortality rate was high in patients with smokers compared to healthy patients (4.22%, the hazard ratio [HR], 1.358; 95% confidence interval [CI], 1.542-1.100; p = .014), it was less than in diabetics (7.04%, HR 1.531, 95% CI: 1.668-1.337, p = .000), and diabetic plus smoker (10.00%, HR, 1.659; 95% CI, 1.763-1.510; p = .000). CONCLUSION: Multiple comorbidities are closely related to the severity of COVID-19 disease progression and the higher mortality rate. Smokers presented as mild cases compared to diabetic and diabetic + smoking patients, who presented as severe to critical cases. Although a higher death rate in smokers was seen compared with healthy patients, this was smaller when compared to diabetic and diabetic + smoking patients.

Preventing and Mitigating Radiology System Failures: A Guide to Disaster Planning.

Disaster planning is a core facet of modern health care practice. Owing to complex infrastructure requirements, radiology departments are vulnerable to system failures that may occur in isolation or during a disaster event when the urgency for and volume of imaging examinations increases. Planning for systems failures helps ensure continuity of service provision and patient care during an adverse event. Hazards to which a radiology department is vulnerable can be identified by applying a systematic approach with recognized tools such as the Hazard, Risk, and Vulnerability Analysis. Potential critical weaknesses within the department are highlighted by the Failure Mode and Effects Analysis tool. Recognizing the potential latent conditions and active failures that may impact systems allows implementation of strategies to prevent failure or to build resilience and mitigate the effects if they happen. Inherent system resilience to an adverse event can be estimated, and the ability of a department to operate during a disaster and the subsequent recovery can be predicted. The main systems at risk in a radiology department are staff, structure, stuff (supplies and/or equipment), and software, although individual issues and solutions within these are department specific. When medical imaging or examination interpretation needs cannot be met in the radiology department, the use of portable imaging modalities and teleradiology can augment the disaster response. All phases of disaster response planning should consider both sustaining operations and the transition back to normal function. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Work of the U.S. Government published under an exclusive license with the RSNA.

Whole genomes redefine the mutational landscape of pancreatic cancer.

Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (variation in chromosomal structure) classified PDACs into 4 subtypes with potential clinical utility: the subtypes were termed stable, locally rearranged, scattered and unstable. A significant proportion harboured focal amplifications, many of which contained druggable oncogenes (ERBB2, MET, FGFR1, CDK6, PIK3R3 and PIK3CA), but at low individual patient prevalence. Genomic instability co-segregated with inactivation of DNA maintenance genes (BRCA1, BRCA2 or PALB2) and a mutational signature of DNA damage repair deficiency. Of 8 patients who received platinum therapy, 4 of 5 individuals with these measures of defective DNA maintenance responded.

High Acuity Therapy Variation Across Pediatric Acute Care Cardiology Units: Results from the Pediatric Acute Care Cardiology Collaborative Hospital Surveys.

We utilized the multicenter Pediatric Acute Care Cardiology Collaborative (PAC3) 2017 and 2019 surveys to describe practice variation in therapy availability and changes over a 2-year period. A high acuity therapies (ATs) score was derived (1 point per positive response) from 44 survey questions and scores were compared to center surgical volume. Of 31 centers that completed the 2017 survey, 26 also completed the 2019 survey. Scores ranged from 11 to 34 in 2017 and 11 to 35 in 2019. AT scores in 2019 were not statistically different from 2017 scores (29/44, IQR 27-32.5 vs. 29.5/44, IQR 27-31, p = 0.9). In 2019, more centers reported initiation of continuous positive airway pressure (CPAP) and Bi-level positive airway pressure (BiPAP) in Acute Care Cardiology Unit (ACCU) (19/26 vs. 4/26, p < 0.001) and permitting continuous CPAP/BiPAP (22/26 vs. 14/26, p = 0.034) compared to 2017. Scores in both survey years were significantly higher in the highest surgical volume group compared to the lowest, 33 ± 1.5 versus 25 ± 8.5, p = 0.046 and 32 ± 1.7 versus 23 ± 5.5, p = 0.009, respectively. Variation in therapy within the ACCUs participating in PAC3 presents an opportunity for shared learning across the collaborative. Experience with PAC3 was associated with increasing available respiratory therapies from 2017 to 2019. Whether AT scores impact the quality and outcomes of pediatric acute cardiac care will be the subject of further investigation using a comprehensive registry launched in early 2019.

Precision Oncology in Surgery: Patient Selection for Operable Pancreatic Cancer.

OBJECTIVE: We aimed to define preoperative clinical and molecular characteristics that would allow better patient selection for operative resection. BACKGROUND: Although we use molecular selection methods for systemic targeted therapies, these principles are not applied to surgical oncology. Improving patient selection is of vital importance for the operative treatment of pancreatic cancer (pancreatic ductal adenocarcinoma). Although surgery is the only chance of long-term survival, 80% still succumb to the disease and approximately 30% die within 1 year, often sooner than those that have unresected local disease. METHOD: In 3 independent pancreatic ductal adenocarcinoma cohorts (total participants = 1184) the relationship between aberrant expression of prometastatic proteins S100A2 and S100A4 and survival was assessed. A preoperative nomogram based on clinical variables available before surgery and expression of these proteins was constructed and compared to traditional measures, and a postoperative nomogram. RESULTS: High expression of either S100A2 or S100A4 was independent poor prognostic factors in a training cohort of 518 participants. These results were validated in 2 independent patient cohorts (Glasgow, n = 198; Germany, n = 468). Aberrant biomarker expression stratified the cohorts into 3 distinct prognostic groups. A preoperative nomogram incorporating S100A2 and S100A4 expression predicted survival and nomograms derived using postoperative clinicopathological variables. CONCLUSIONS: Of those patients with a poor preoperative nomogram score, approximately 50% of patients died within a year of resection. Nomograms have the potential to improve selection for surgery and neoadjuvant therapy, avoiding surgery in aggressive disease, and justifying more extensive resections in biologically favorable disease.

Multicenter Analysis of Truncal Valve Management and Outcomes in Children with Truncus Arteriosus.

Truncal valve management in patients with truncus arteriosus is a clinical challenge, and indications for truncal valve intervention have not been defined. We sought to evaluate truncal valve dysfunction and primary valve intervention in patients with truncus arteriosus and determine risk factors for later truncal valve intervention. We conducted a retrospective cohort study of children who underwent truncus arteriosus repair at 15 centers between 2009 and 2016. Multivariable competing risk analysis was performed to determine risk factors for later truncal valve intervention. We reviewed 252 patients. Forty-two patients (17%) underwent truncal valve intervention during their initial surgery. Postoperative extracorporeal support, CPR, and operative mortality for patients who underwent truncal valve interventions were statistically similar to the rest of the cohort. Truncal valve interventions were performed in 5 of 64 patients with mild insufficiency; 5 of 16 patients with mild-to-moderate insufficiency; 17 of 35 patients with moderate insufficiency; 5 of 9 patients with moderate-to-severe insufficiency; and all 10 patients with severe insufficiency. Twenty patients (8%) underwent later truncal valve intervention, five of whom had no truncal valve intervention during initial surgical repair. Multivariable analysis revealed truncal valve intervention during initial repair (HR 11.5; 95% CI 2.5, 53.2) and moderate or greater truncal insufficiency prior to initial repair (HR 4.0; 95% CI 1.1, 14.5) to be independently associated with later truncal valve intervention. In conclusion, in a multicenter cohort of children with truncus arteriosus, 17% had truncal valve intervention during initial surgical repair. For patients in whom variable truncal valve insufficiency is present and primary intervention was not performed, late interventions were uncommon. Conservative surgical approach to truncal valve management may be justifiable.

A Survey on the Security of Low Power Wide Area Networks: Threats, Challenges, and Potential Solutions.

Low power wide area network (LPWAN) is among the fastest growing networks in Internet of Things (IoT) technologies. Owing to varieties of outstanding features which include long range communication and low power consumption, LPWANs are fast becoming the most widely deployed connectivity standards in IoT domain. However, this promising network are exposed to various security and privacy threats and challenges. For reliable connectivity within networks, the security and privacy challenges need to be effectively addressed with proper mitigation protocol in place. In this paper, a comprehensive review on the security feature of LPWAN is presented. The paper mainly focuses on analyzing LPWAN's key cybersecurity architecture and it present a significant emphasis on how the LPWAN is highly attractive to intruders and attackers. This paper aims at summarizing recent research works on key LPWAN security challenges such as replay attack, denial-of-service attack, worm hole attack, and eavesdropping attack, the effect of the attacks, and most importantly the various approaches proposed in the literature for the attacks' mitigation. The paper concludes by highlighting major research gaps and future directions for the successful deployment of LPWAN.

A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges.

Long-Range Wide Area Network (LoRaWAN) is a fast-growing communication system for Low Power Wide Area Networks (LPWAN) in the Internet of Things (IoTs) deployments. LoRaWAN is built to optimize LPWANs for battery lifetime, capacity, range, and cost. LoRaWAN employs an Adaptive Data Rate (ADR) scheme that dynamically optimizes data rate, airtime, and energy consumption. The major challenge in LoRaWAN is that the LoRa specification does not state how the network server must command end nodes pertaining rate adaptation. As a result, numerous ADR schemes have been proposed to cater for the many applications of IoT technology, the quality of service requirements, different metrics, and radio frequency (RF) conditions. This offers a challenge for the reliability and suitability of these schemes. This paper presents a comprehensive review of the research on ADR algorithms for LoRaWAN technology. First, we provide an overview of LoRaWAN network performance that has been explored and documented in the literature and then focus on recent solutions for ADR as an optimization approach to improve throughput, energy efficiency and scalability. We then distinguish the approaches used, highlight their strengths and drawbacks, and provide a comparison of these approaches. Finally, we identify some research gaps and future directions.

Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration.

The Internet of Things (IoT) is an emerging paradigm that enables many beneficial and prospective application areas, such as smart metering, smart homes, smart industries, and smart city architectures, to name but a few. These application areas typically comprise end nodes and gateways that are often interconnected by low power wide area network (LPWAN) technologies, which provide low power consumption rates to elongate the battery lifetimes of end nodes, low IoT device development/purchasing costs, long transmission range, and increased scalability, albeit at low data rates. However, most LPWAN technologies are often confronted with a number of physical (PHY) layer challenges, including increased interference, spectral inefficiency, and/or low data rates for which cognitive radio (CR), being a predominantly PHY layer solution, suffices as a potential solution. Consequently, in this article, we survey the potentials of integrating CR in LPWAN for IoT-based applications. First, we present and discuss a detailed list of different state-of-the-art LPWAN technologies; we summarize the most recent LPWAN standardization bodies, alliances, and consortia while emphasizing their disposition towards the integration of CR in LPWAN. We then highlight the concept of CR in LPWAN via a PHY-layer front-end model and discuss the benefits of CR-LPWAN for IoT applications. A number of research challenges and future directions are also presented. This article aims to provide a unique and holistic overview of CR in LPWAN with the intention of emphasizing its potential benefits.