Facial recognition by cloud-based APIs following surgically assisted rapid maxillary expansion

INTRODUCTION

This study aimed to investigate whether the facial soft tissue changes of individuals who had undergone surgically assisted rapid maxillary expansion (SARME) would be detected by three different well-known facial biometric recognition applications.

METHODS

To calculate similarity scores, the pre- and postsurgical photographs of 22 patients who had undergone SARME treatment were examined using three prominent cloud computing-based facial recognition application programming interfaces (APIs): AWS Rekognition (Amazon Web Services, Seattle, WA, USA), Microsoft Azure Cognitive (Microsoft, Redmond, WA, USA), and Face++ (Megvii, Beijing, China). The pre- and post-SARME photographs of the patients (relaxed, smiling, profile, and semiprofile) were used to calculate similarity scores using the APIs. Friedman's two-way analysis of variance and the Wilcoxon signed-rank test were used to compare the similarity scores obtained from the photographs of the different aspects of the face before and after surgery using the different programs. The relationship between measurements on lateral and posteroanterior cephalograms and the similarity scores was evaluated using the Spearman rank correlation.

RESULTS

The similarity scores were found to be lower with the Face++ program. When looking at the photo types, it was observed that the similarity scores were higher in the smiling photos. A statistically significant difference in the similarity scores (P < 0.05) was found between the relaxed and smiling photographs using the different programs. The correlation between the cephalometric and posteroanterior measurements and the similarity scores was not significant (P > 0.05).

CONCLUSION

SARME treatment caused a significant change in the similarity scores calculated with the help of three different facial recognition programs. The highest similarity scores were found in the smiling photographs, whereas the lowest scores were found in the profile photographs.

Accelerating genomic workflows using NVIDIA Parabricks

BACKGROUND

As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to accelerate genomic workflows by orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster. We benchmarked six variant calling pipelines, including two germline callers (HaplotypeCaller and DeepVariant) and four somatic callers (Mutect2, Muse, LoFreq, SomaticSniper).

RESULTS

We achieved up to 65 × acceleration with germline variant callers, bringing HaplotypeCaller runtimes down from 36 h to 33 min on AWS, 35 min on GCP, and 24 min on the NVIDIA DGX. Somatic callers exhibited more variation between the number of GPUs and computing platforms. On cloud platforms, GPU-accelerated germline callers resulted in cost savings compared with CPU runs, whereas some somatic callers were more expensive than CPU runs because their GPU acceleration was not sufficient to overcome the increased GPU cost.

CONCLUSIONS

Germline variant callers scaled well with the number of GPUs across platforms, whereas somatic variant callers exhibited more variation in the number of GPUs with the fastest runtimes, suggesting that, at least with the version of Parabricks used here, these workflows are less GPU optimized and require benchmarking on the platform of choice before being deployed at production scales. Our study demonstrates that GPUs can be used to greatly accelerate genomic workflows, thus bringing closer to grasp urgent societal advances in the areas of biosurveillance and personalized medicine.

Ab initio structure solution of proteins at atomic resolution using charge-flipping techniques and cloud computing

Large protein structures at atomic resolution can be solved in minutes using charge-flipping techniques operating on hundreds of virtual machines (computers) on the Amazon Web Services cloud-computing platform driven by the computer programs TOPAS or TOPAS-Academic at a small financial cost. The speed of operation has allowed charge-flipping techniques to be investigated and modified, leading to two strategies that can solve a large range of difficult protein structures at atomic resolution. Techniques include the use of space-group symmetry restraints on the electron density as well as increasing the intensity of a randomly chosen high-intensity electron-density peak. It is also shown that the use of symmetry restraints increases the chance of finding a solution for low-resolution data. Finally, a flipping strategy that negates `uranium atom solutions' has been developed for structures that exhibit such solutions during charge flipping.

Agile clinical research: A data science approach to scrumban in clinical medicine

The COVID-19 pandemic has required greater minute-to-minute urgency of patient treatment in Intensive Care Units (ICUs), rendering the use of Randomized Controlled Trials (RCTs) too slow to be effective for treatment discovery. There is a need for agility in clinical research, and the use of data science to develop predictive models for patient treatment is a potential solution. However, rapidly developing predictive models in healthcare is challenging given the complexity of healthcare problems and the lack of regular interaction between data scientists and physicians. Data scientists can spend significant time working in isolation to build predictive models that may not be useful in clinical environments. We propose the use of an agile data science framework based on the Scrumban framework used in software development. Scrumban is an iterative framework, where in each iteration larger problems are broken down into simple do-able tasks for data scientists and physicians. The two sides collaborate closely in formulating clinical questions and developing and deploying predictive models into clinical settings. Physicians can provide feedback or new hypotheses given the performance of the model, and refinement of the model or clinical questions can take place in the next iteration. The rapid development of predictive models can now be achieved with increasing numbers of publicly available healthcare datasets and easily accessible cloud-based data science tools. What is truly needed are data scientist and physician partnerships ensuring close collaboration between the two sides in using these tools to develop clinically useful predictive models to meet the demands of the COVID-19 healthcare landscape.

HRV-Spark: Computing Heart Rate Variability Measures Using Apache Spark

Heart rate variability (HRV) analysis has been serving as a significant promising marker in clinical research over the last few decades. The rapidly growing heart rate data generated from various devices, particularly the electrocardiograph (ECG), need to be stored properly and processed timely. There is a pressing need to develop efficient approaches for performing HRV analyses based on ECG signals. In this paper, we introduce a cloud computing approach (called HRV-Spark) to compute HRV measures in parallel by leveraging Apache Spark and a QRS detection algorithm in [1]. We ran HRV-Spark on Amazon Web Services (AWS) clusters using large-scale datasets in the National Sleep Research Resource. We evaluated the performance and scalability of HRV-Spark in terms of the number of computing nodes in the AWS cluster, the size of the input datasets, and the hardware configuration of the computing nodes. The results show that HRV-Spark is an efficient and scalable approach for computing HRV measures.

Developing a Reproducible Microbiome Data Analysis Pipeline Using the Amazon Web Services Cloud for a Cancer Research Group: Proof-of-Concept Study

BACKGROUND

Cloud computing for microbiome data sets can significantly increase working efficiencies and expedite the translation of research findings into clinical practice. The Amazon Web Services (AWS) cloud provides an invaluable option for microbiome data storage, computation, and analysis.

OBJECTIVE

The goals of this study were to develop a microbiome data analysis pipeline by using AWS cloud and to conduct a proof-of-concept test for microbiome data storage, processing, and analysis.

METHODS

A multidisciplinary team was formed to develop and test a reproducible microbiome data analysis pipeline with multiple AWS cloud services that could be used for storage, computation, and data analysis. The microbiome data analysis pipeline developed in AWS was tested by using two data sets: 19 vaginal microbiome samples and 50 gut microbiome samples.

RESULTS

Using AWS features, we developed a microbiome data analysis pipeline that included Amazon Simple Storage Service for microbiome sequence storage, Linux Elastic Compute Cloud (EC2) instances (ie, servers) for data computation and analysis, and security keys to create and manage the use of encryption for the pipeline. Bioinformatics and statistical tools (ie, Quantitative Insights Into Microbial Ecology 2 and RStudio) were installed within the Linux EC2 instances to run microbiome statistical analysis. The microbiome data analysis pipeline was performed through command-line interfaces within the Linux operating system or in the Mac operating system. Using this new pipeline, we were able to successfully process and analyze 50 gut microbiome samples within 4 hours at a very low cost (a c4.4xlarge EC2 instance costs $0.80 per hour). Gut microbiome findings regarding diversity, taxonomy, and abundance analyses were easily shared within our research team.

CONCLUSIONS

Building a microbiome data analysis pipeline with AWS cloud is feasible. This pipeline is highly reliable, computationally powerful, and cost effective. Our AWS-based microbiome analysis pipeline provides an efficient tool to conduct microbiome data analysis.

cryoem-cloud-tools: A software platform to deploy and manage cryo-EM jobs in the cloud

Access to streamlined computational resources remains a significant bottleneck for new users of cryo-electron microscopy (cryo-EM). To address this, we have developed tools that will submit cryo-EM analysis routines and atomic model building jobs directly to Amazon Web Services (AWS) from a local computer or laptop. These new software tools ("cryoem-cloud-tools") have incorporated optimal data movement, security, and cost-saving strategies, giving novice users access to complex cryo-EM data processing pipelines. Integrating these tools into the RELION processing pipeline and graphical user interface we determined a 2.2 Å structure of ß-galactosidase in ∼55 h on AWS. We implemented a similar strategy to submit Rosetta atomic model building and refinement to AWS. These software tools dramatically reduce the barrier for entry of new users to cloud computing for cryo-EM and are freely available at cryoem-tools.cloud.

Safety and Lack of Negative Effects of Wearable Augmented-Reality Social Communication Aid for Children and Adults with Autism

There is a growing interest in the use of augmented reality (AR) to assist children and adults with autism spectrum disorders (ASD); however, little investigation has been conducted into the safety of AR devices, such as smartglasses. The objective of this report was to assess the safety and potential negative effects of the Empowered Brain system, a novel AR smartglasses-based social communication aid for people with ASD. The version of the Empowered Brain in this report utilized Google Glass (Google, Mountain View, CA, USA) as its hardware platform. A sequential series of 18 children and adults, aged 4.4 to 21.5 years (mean 12.2 years), with clinically diagnosed ASD of varying severity used the system. Users and caregivers were interviewed about the perceived negative effects and design concerns. Most users were able to wear and use the Empowered Brain (n = 16/18, 89%), with most of them reporting no negative effects (n = 14/16, 87.5%). Caregivers observed no negative effects in users (n = 16/16, 100%). Most users (77.8%) and caregivers (88.9%) had no design concerns. This report found no major negative effects in using an AR smartglasses-based social communication aid across a wide age and severity range of people with ASD. Further research is needed to explore longer-term effects of using AR smartglasses in this population.

META-pipe cloud setup and execution

META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.

META-pipe cloud setup and execution

META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.

META-pipe cloud setup and execution

META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.

Survalytics: An Open-Source Cloud-Integrated Experience Sampling, Survey, and Analytics and Metadata Collection Module for Android Operating System Apps

BACKGROUND

We describe here Survalytics, a software module designed to address two broad areas of need. The first area is in the domain of surveys and app analytics: developers of mobile apps in both academic and commercial environments require information about their users, as well as how the apps are being used, to understand who their users are and how to optimally approach app development. The second area of need is in the field of ecological momentary assessment, also referred to as experience sampling: researchers in a wide variety of fields, spanning from the social sciences to psychology to clinical medicine, would like to be able to capture daily or even more frequent data from research subjects while in their natural environment.

OBJECTIVE

Survalytics is an open-source solution for the collection of survey responses as well as arbitrary analytic metadata from users of Android operating system apps.

METHODS

Surveys may be administered in any combination of one-time questions and ongoing questions. The module may be deployed as a stand-alone app for experience sampling purposes or as an add-on to existing apps. The module takes advantage of free-tier NoSQL cloud database management offered by the Amazon Web Services DynamoDB platform to package a secure, flexible, extensible data collection module. DynamoDB is capable of Health Insurance Portability and Accountability Act compliant storage of personal health information.

RESULTS

The provided example app may be used without modification for a basic experience sampling project, and we provide example questions for daily collection of blood glucose data from study subjects.

CONCLUSIONS

The module will help researchers in a wide variety of fields rapidly develop tailor-made Android apps for a variety of data collection purposes.